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The production functions of wheat production in Turkey

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The efficiency of inputs’ level and monetary size of production factors were determined for wheat production in Turkey. In the study Cobb-Douglas, type production function was used and multi-determination coefficient of acquired estimating equation (R2) was estimated at 0.825. It has been proved that there is increasing return to scale (Σβi=1.089) in wheat production, based on the sum of elasticity coefficient of variables in the function. The coefficients of factors have been considered that when some factors such as land quantity, fertilizer cost and pesticide cost increase the wheat production also increase due to increasing return of scale. In the provinces where this research has been conducted, the average yield of wheat varies between 1.893-4.384 ton ha-1. For the average research provinces, it has been proved that gross production value of the wheat varies between 533.83€ - 1192.45€ ha-1 and gross profit varies between 205.43€-826.95€ ha-1. Also agricultural subsidies which were taken by the provinces, according to their yield per unit area, varies between 95.18€ - 145.00€ ha-1. It has been concluded from the research that despite of the support payments to encourage the agricultural production, the competitive power of Turkey is low in wheat and agricultural support unit price is insufficient because of the higher production cost of wheat in proportion to other countries.
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240
THE PRODUCTION FUNCTIONS OF WHEAT PRODUCTION
IN TURKEY
A. SEMERCI1*, A. MAZID2, K. N. AMEGBETO2, M. KESER2, A. MORGOUNOV3, K. PEKER4, A.
BAGCI4, M. AKIN5, M. KUCUKCONGAR5, M. KAN5, S. KARABAK5, A. ALTIKAT5 and S. YAKTU-
BAY5
¹ Trakya Agricultural Research Institute, PO Box 16, 22100, Edirne, Turkey
2 International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria
3International Maize and Wheat Improvement Centre (CIMMYT) – Mexico
4 Selcuk University, Konya, Turkey
5General Directorate of Agricultural Research, Ministry of Agriculture and Rural Affairs, Ankara, Turkey
Abstract
SEMERCI, A., A. MAZID, K. N AMEGBETO, M. KESER, A. MORGOUNOV, K. PEKER, A. BAGCI,
M. AKIN, M. KUCUKCONGAR, M. KAN, S. KARABAK, A. ALTIKAT and S. YAKTUBAY, 2012.
The production functions of wheat production in Turkey. Bulg. J. Agric. Sci., 18: 240-253
In this research, the efciency of inputs’ level and monetary size of production factors were determined for
wheat production in Turkey. In the study Cobb-Douglas, type production function was used and multi-determina-
tion coefcient of acquired estimating equation (R2) was estimated at 0.825. It has been proved that there is increas-
ing return to scale (∑βi=1.089) in wheat production, based on the sum of elasticity coefcient of variables in the
function. The coefcients of factors have been considered that when some factors such as land quantity, fertilizer
cost and pesticide cost increase the wheat production also increase due to increasing return of scale. In the prov-
inces where this research has been conducted, the average yield of wheat varies between 1.893-4.384 ton ha-1. For
the average research provinces, it has been proved that gross production value of the wheat varies between 533.83€
- 1192.45€ ha-1 and gross prot varies between 205.43€-826.95€ ha-1. Also agricultural subsidies which were taken
by the provinces, according to their yield per unit area, varies between 95.18€ - 145.00€ ha-1. It has been concluded
from the research that despite of the support payments to encourage the agricultural production, the competitive
power of Turkey is low in wheat and agricultural support unit price is insufcient because of the higher production
cost of wheat in proportion to other countries.
Key words: Wheat, Input Use, Production Function, Efciency, Subsidy, Turkey
Bulgarian Journal of Agricultural Science, 18 (No 2) 2012, 240-253
Agricultural Academy
* Corresponding author: E-mail: arifsemerci69@gmail.com
Introduction
The wheat ( Triticum aestivum L. em Thell) ,
which has the widest adaptation capacity among
cereal species, has an important role in human nu-
trition (Briggle and Curtis, 1987). According to the
data on 2009 year, Turkey ranks among the rst
11 countries by 3.02 % in the world (FAO, 2011).
The Production Functions of Wheat Production in Turkey 241
With the respect of cultivated area and production
quantity in production pattern, wheat is an impor-
tant product for Turkey by the socially and eco-
nomically ways (Ozcelik and Ozer, 2006).
Along with the studies conducted in the re-
search institutes of Ministry of Agriculture and
Rural Affair (MARA) and improvement programs
of agricultural faculties, the researches on the
growing technique of the improved species were
also important in the aspect of the increase ac-
quired in wheat production per unit area in Tur-
key. In this context the studies about wheat variet-
ies and lines, used in wheat production in Turkey,
have been conducted in terms of both growing
technique and improvement programs (Genctan
and Balkan, 2008; Yagmur and Kaydan, 2008;
Aydin et al., 2005; Baser et al., 2007; Gecit and
Cakir, 2006). The highest performance has been
obtained from improved wheat species is closely
related with optimum use of the factors in produc-
tion stage, along with the climatic features of the
wheat production area.
The basic objective of the agricultural enter-
prises at agricultural production is to increase the
prot by using land, water, plant, and manpower
resources in a productive and compatible way
within the enterprises’ own conditions and oppor-
tunities. The providing of production factors by
the most suitable price and using them in optimum
level have an effect on increasing the productivity
and reducing the costs in the enterprises.
Due to the undercapitalization of the enterprises
and lack of technical information, the producers
are not able to use agricultural production factors
in optimum level and this fact affects the yield and
producer income negatively. For that reason the
studies that determine the input use level of the
producers for agricultural products in a specic
model and that show which input must be used at
which level, are required (Gundogmus, 1998).
There have been some studies about determin-
ing the technical efciency of input use in wheat
production around the world (Battese et al., 1996;
Boshrabadi et al., 2008; Hadley, 2006; Coelli et
al., 2003; Conradie et al., 2006; Zhu and Lansink,
2010; Hussian et al., 2004). In Turkey where the
wheat has been an important role on state econo-
my, some studies have been made on determining
the technical efciency level of the inputs (Ozs-
abuncuoglu, 1998; Gundogmus, 1998; Bayramo-
glu and Oguz, 2005).
One of the criteria that has been used for evalu-
ating the agricultural production activities accord-
ing to the order of priority in producer conditions
and for determining the protability of producers
is Gross Prot. In this research, the differences in
input use in wheat production have been examined
statistically in 5 provinces of Turkey, located at the
different geographic regions where approximately
22% of the wheat production has been made and
the efciency coefcients of factors have been cal-
culated with the help of marginal yield and mar-
ginal income coefcients. The data obtained, have
been compared to former research ndings and re-
lated conclusions have been made.
Literature Abstracts
In the scope of the research made in Ankara on
wheat production, it has been proved that multi-
determination coefcient of estimating equation
(R2) is 0.998 and the sum of production elastic-
ity coefcient (∑βi) is 1.033. In the enterprises it
has been concluded that pure nitrogenous fertilizer
is used excessively and it must be reduced. In the
equation created in the extent of the research, it has
been observed that production area (0.713) is the
most effective factor in wheat production (Gun-
dogmus, 1998).
In the research conducted in South Eastern
Anatolian Region, the functions such as multiple
linear, quadratic and Cobb-Douglas production
function have been used for functional analysis
of wheat production. In the estimating equation
which has been made, the sum of production elas-
A. Semerci, A. Mazid, K. N. Amegbeto, M. Keser, A. Morgounov,
242 K. Peker, A. Bagci, M. Akin, M. Kucukcongar, M. Kan, S. Karabak, A. Altikat and S. Yaktubay
ticity coefcient (∑βi) has been found below 1.
The production area, chemical fertilizer and rain-
fall factors which are effective on wheat produc-
tion have been examined within the research. As
a result of the research, it has been concluded that
to provide an increase in the wheat production,
input use level must be increased (Ozsabuncuo-
glu, 1998).
In the research conducted in Konya, the rela-
tion between wheat production quantity and pro-
duction factors has been observed with the help of
Cobb-Douglas function type. In the model, it has
been determined multi-determining coefcient
(R2) is 0.935 and the most effective factor on pro-
duction quantity is irrigation. In the research the
sum of production elasticity coefcient of vari-
ables used in production has been found to be be-
low 1. As a result it has been concluded that irri-
gation number and land factor must be increased
to provide an increase in wheat production (Bay-
ramoglu and Oguz, 2005).
In the research conducted in Tokat, the factors
which are effective on wheat production have
been examined. The multi-determination coef-
cient of the created equation has been observed to
be (R2) 0.879 and the sum of elasticity coefcient
is (∑βi) 1.635. In the research it has been proved
that the most effective factor on wheat produc-
tion is seed input. For the fact that the sum of the
efciency coefcient of the factors in estimating
equation is below 1, it has been concluded that
there must be a restriction in input use (Akcay
and Uzunoz, 1999).
In the similar research conducted in Kirklareli,
the multi-determination coefcient (R2) of esti-
mating equation in wheat production has been
calculated as 0.966 and the sum of elasticity coef-
cient (∑βi) calculated as 1.079. It has also been
observed that the most effective factor on wheat
production is seed input (0.763). In the research,
efciency coefcients of the inputs have been
evaluated and it has been emphasized that there
must be a restriction in seed, pesticide and fertil-
izer factors (Semerci, 1998).
In the cost research conducted in Thrace Region,
it has been calculated that input cost is 337.66€,
labor cost is 306.19€, harvest and marketing cost
is 91.57€ for 5.050 ton ha-1 wheat yield. In the re-
search it has been determined that variable cost
is 764.84€, xed cost is 260.00€ and total crop
cost is 938.99€. In the research it has also been
determined that the gross production value of the
wheat, including the subsidies, is 1358.35€, gross
prot is 593.51€ and net prot is 333.51€ (Kum-
bar and Unakitan, 2011). The literature informa-
tion about input use level in wheat production has
been given in related part as comparatively with
research ndings.
Material and Method
The material of the research has been obtained
from the data acquired in the extent of “Adaption
and Impacts of Improved Winter and Spring Wheat
Varieties in Turkey” which has been conducted
by the cooperation with Ministry of Agricultural
and Rural Affairs (MARA) General Directorate
of Agricultural Research (GDAR), International
Center for Agricultural Research in the Dry Areas
(ICARDA) and International Winter Wheat Im-
provement Program (IWWIP). In the research the
cross sectional data of the 2006/2007 production
period which has been acquired from 781 wheat
enterprises were used. The climatic data used in
the research have been acquired from the General
Directorate of State Meteorology Affairs and the
data of the support payment in wheat productions
have been obtained from MARA (TSMS, 2009;
MARA, 2011).
Based on the denition of agricultural zones by
Turkish Statistical Institute, the general character-
istics of the provinces on the research area where
the questionnaire has been applied are summarized
shortly below (Mazid et al., 2009).
The Production Functions of Wheat Production in Turkey 243
Ankara is within the central north zone with
a continental climate and annual rainfall of 375
mm. The production system for cereals, food and
forage legumes is predominantly rainfed with ex-
tensive rearing of small ruminants, and intensive
dairy cattle. Edirne is in the Marmara and Thrace
zone with an average rainfall of 700 mm per year.
Wheat and other cereals are produced, also sun-
ower, and vegetables. Adana is part of the Medi-
terranean agricultural zone in the western coastal
area of the country, with average rainfall up to 700
mm per year. Cereals are produced under rainfed
conditions or irrigation. Diyarbakir is in the south-
east zone with large fertile plains in the southern
part. The production system is mainly rainfed, al-
though the South East Anatolia (GAP) project has
invested in one of the biggest irrigation scheme in
the zone. Konya is part of the central south zone
having an average rainfall of 350 mm per year and
80-100 days of frost. Crop production is mainly
under rainfed cultivation.
Sampling Method
Central Anatolia, Thrace Region, Southeastern
Anatolia and Cukurova Regions are signicant
wheat production areas even though wheat pro-
duction spreads through the countrywide in Tur-
key (Kumbasaroglu and Dagdemir, 2010). The
research has been conducted in the regions where
wheat productions are intensive and in Adana, An-
kara, Diyarbakir, Edirne and Konya which have
different agro-ecological conditions. Given prov-
inces consists 21.98% of Turkey’s wheat produc-
tion in 2007 (TURKSTAT, 2011). In the research,
Multi-Stage Stratied Random Sampling Method
has been used to determine the sampling volume.
Distribution of the questionnaires by provinces
used in the research has been given in Table 1.
Production quantities, wheat cultivated areas
and wheat producer numbers of the provinces in
the research area have been taken into consider-
ation in determining the number of the applied
questionnaires and their distribution by provinc-
es. Because of this applied method, it has been
determined to apply 781 questionnaires in the re-
search.
The Method Used in Functional Analysis
A great deal of functional forms has been de-
ned for production process (Grifn et al., 1987).
Cobb-Douglas production function is one of the
most widespread function types used particularly
in the studies of agricultural-economics for this
reason (Debertin, 1986) Cobb-Douglas function
type has two sided logarithmic structure. In the
model, the coefcient of every X variable measures
the (partial) elasticity of the dependant Y variable
in proportion to that variable. Additionally, in the
Cobb-Douglas production function, sum of the
estimated elasticity coefcients has been used as
an indicator of returns to scale (Heady and Dillon,
1972). The form related to Cobb-Douglas function
type is given below:
n
Y = A ΠXi
ßi, ßi > 0 i = 1,2 ... n ( 1 )
i=1
where Y is the output, and X a vector of essential
inputs used in production, and n is number of in-
puts used. A is the combined effects on the pro-
duction function of all inputs (rainfall, disease out-
breaks, etc.) that are not under the strict control of
the farmer. Empirically, a logarithmic transforma-
tion in the following format was made, and dummy
variable included to the equation to distinguish the
impact of the rainfed or irrigated system on wheat
production (Mazid et al., 2009).
In the equality, Y symbolizes wheat production,
Xi symbolizes variables such as seed, fertilizer,
pesticide, Dj symbolizes the dummy variable re-
n j
ln(Y ) = ln(A) + ∑ ßi ln (Xi) + ∑ δjDj + ε,
i=1 j=1
ßi> 0 i =1,2,....n, j=1,2,... J
( 2 )
A. Semerci, A. Mazid, K. N. Amegbeto, M. Keser, A. Morgounov,
244 K. Peker, A. Bagci, M. Akin, M. Kucukcongar, M. Kan, S. Karabak, A. Altikat and S. Yaktubay
lated to the production in watery and dry condi-
tion (1: production in watery condition, 0: produc-
tion in dry condition) and ε symbolizes error term
of equation. In the study, Cobb-Douglas function
type has been used to dene the relations between
wheat production quantity (Y) and the inputs used
in production (Xi). The variables placing in the
model are given below:
Ln Y : wheat production quantity (kg enterprise).
Ln X1 : production area (ha-1 enterprise)
Ln X2 : seed cost (€ enterprise).
Ln X3 : chemical fertilizer cost (€ enterprise)
Ln X4 : pesticide cost (€ enterprise)
Ln X5 : precipitation in the wheat production pe-
riod (mm)
Ln X6 : Dummy variable for production system of
the wheat production (1=irrigated, 0 = rain fed)
In the research, elasticity coefcient belonging
to the inputs used in wheat production, marginal
yield, marginal product value, marginal productiv-
ity coefcient have been calculated and the factors
have been commented. In the research, the value
of marginal yield is obtained as a result of multi-
plication of elasticity coefcient of related factor
(Xi) and the value calculated as a consequence of
division of geometrical mean of production quan-
tity (Y) to the geometrical mean of related factor
(Xi). Marginal income is obtained because of mul-
tiplication of related factor (Xi) and product price.
Marginal efciency coefcient is obtained as a
consequence of division of related factors (Xi)
marginal income to the unit price of the same fac-
tor (Karkacier, 2001)
The test of Tukey HSD has been used to deter-
mine the differences between the input quantities
used in wheat production among the provinces that
analyzed enterprises are situated (Ural and Kilic,
2006; Altunistik et al., 2007; Green et al., 2000).
Results and Discussion
As results of data obtained from the enterpris-
es, which have been applied questionnaires in the
research area, variable costs of wheat have been
calculated and stated in Table 2. When Table 2 is
examined, it It has been understood that there are
some cost differences within provinces such as
25€ is the difference in pesticide cost, 51€ is the
difference in fertilizer cost and the difference in
seed cost used per unit area in wheat production is
approximately 20€.
When the input costs of investigated enterprises
are considered, it has been obviously seen that the
highest input cost is in Adana province (280.06€)
the lowest input cost is in the enterprises of Ankara
province (193.73€). When the input costs are eval-
uated together with labor costs, it is understood
that total variable cost is seen at the highest level
in the enterprises of Adana province, at the lowest
level in the enterprises of Konya province.
Agricultural production has been subsidies with
a variety of policies executed especially since the
beginning of 2000’s. The utilization level of pro-
ducers from the supports show differences accord-
ing to production quantity and production area.
Wheat producers also utilize from fertilizer, fuel
and soil analysis supports as area based. Addition-
ally, in the scope of encouragement of production,
they utilize from bounty (premium) support depen-
dent upon product quantity. Wheat yield, supports
Table 1
The distribution of sampling volume by provinces
in the research area
Province
District
Communities
Appl. Quest.
Numbers
Proportion, %
Adana 7 27 130 16.65
Ankara 6 27 130 16.65
Diyarbakır 7 49 130 16.65
Edirne 8 15 90 11.52
Konya 10 52 301 38.53
Total 38 170 781 100.00
The Production Functions of Wheat Production in Turkey 245
Table 2
Unit costs of wheat production in the research area
Cost elements Provinces
Adana Ankara Diyarbakir Edirne Konya
Input Use
Seed cost (€ ha-1) 91.41 80.12 83.90 73.54 95.51
Fertilizer cost (€ ha-1) 152.76 104.56 142.60 154.54 122.86
Pesticide cost (€ ha-1) 35.89 9.05 16.72 14.43 5.57
Labor costs
Ploughing (€ ha-1) 109.63 111.30 129.13 99.81 84.92
Seeding (€ ha-1) 20.86 23.61 23.04 33.02 22.72
Irrigation (€ ha-1) 30.81 29.17 35.46 23.23 26.29
Labor of agri-ght (€ ha-1) 17.64 19.88 27.80 0.00 13.28
Labor of fertilizing (€ ha-1) 15.30 14.80 13.34 14.48 15.40
Harvesting and threshing (€ ha-1) 36.20 31.09 34.16 47.74 32.66
Total variable cost (€ ha-1) 510.50 423.58 506.15 460.79 419.21
Table 3
Gross prot of wheat production in the research area
Indicators Provinces
Adana Ankara Diyarbakir Edirne Konya
Yield (ton ha) 4 384 1 893 3 702 4 149 2 456
Product price (€ ton) 252.00 262.00 256.00 256.00 291.00
Subsidies
Bounty (price) support (€ ton) 20.00 20.00 20.00 20.00 20.00
Fertilizer support (€ ha) 12.20 12.20 12.20 12.20 12.20
Fuel support (€ ha) 16.50 16.50 16.50 16.50 16.50
Sertied seed support (€ ha) 28.62 28.62 28.62 28.62 28.62
Total supports (€ ha) 145.00 95.18 131.36 140.30 106.44
Gross production value (€ ha) 1192.45 533.83 1021.75 1145.12 763.82
Total variable cost (€ ha) 510.50 423.58 506.15 460.79 419.21
Gross prot* (€ ha) 681.95 110.25 515.60 684.33 344.61
Gross prot** (€ ha) 826.95 205.43 646.96 824.63 451.05
(*).Total subsidies are excluded.
(**).Total subsidies are included.
and gross prot values in analyzed enterprises have
been stated in Table 3 by provinces.
There have been signicant differences in the
yield value obtained per unit area in analyzed en-
terprises. When the yield value within provinces
are compared, it has been determined that there
are statistically differences at 1% importance level
between other provinces while Adana and Edirne
don’t have any differences statistically. This condi-
tion also shows similarities in the point of utiliza-
tion level from agricultural subsidies.
While wheat yield is above 4 ton ha-1 in the
enterprises in of Adana and Edirne, it is below 2
ton ha-1 in the enterprises of Ankara province. This
A. Semerci, A. Mazid, K. N. Amegbeto, M. Keser, A. Morgounov,
246 K. Peker, A. Bagci, M. Akin, M. Kucukcongar, M. Kan, S. Karabak, A. Altikat and S. Yaktubay
condition affects especially the utilization level of
enterprises from bounty (premium) support sig-
nicantly. When total supports are taken into con-
sideration, it has been determined that while wheat
gross prot is more than 800€ in the enterprises of
Adana and Edirne, it is a little bit more than 200€
in the enterprises of Ankara.
It has been seen that 594.40€ ha-1 gross prot
calculated in a research conducted for wheat cost
in 2007 in Thrace region which is one of the most
important wheat production area in Turkey, over-
laps with the data of Ankara and Edirne (Kum-
bar and Unakitan, 2011). Kumbar and Unakitan
(2011) have calculated the wheat cost in their re-
search as 184.97€ ton. By the same year, the costs
in wheat production in other important countries
are as such: 208.15€ ton in USA, 136.50€ ton in
Canada, 148.10€ ton in Australia, 151.17€ ton in
China, 132.85€ ton in Russian Federation, 115.18€
ton in Ukraine, 176.86€ ton in India (FAO, 2011).
When the production costs in the countries, which
rank among the leading in wheat production and
agriculture, are compared with Turkey’s, it has
been understood that the wheat cost produced in
Turkey is relatively high. This condition shows
that Turkey cannot compete with other countries in
wheat production and agriculture in the aspect of
cost element despite agricultural support payment.
According to a research related with this issue, it
has been concluded that fuel and fertilizer support
are insufcient in the scope of agricultural support
(Ozcelik and Ozer, 2006)
The level of input use in wheat production has been
examined by provinces in order to determine the dif-
ferences stem from yield in the analyzed enterprises.
The input quantity used per area in wheat production
in enterprises within the research is stated in Table
4. When coefcient of variation (CV) belonging to
input quantities used per unit area in wheat produc-
tion by provinces is examined, in the aspect of seed
quantity and the use level of pure phosphorous fer-
tilizer, it has been understood that other provinces
don’t have any signicant differences, if the enter-
prises in Diyarbakir province are excluded.
It has been proved that there are signicant
differences between the variation coefcient be-
longing to the pesticide quantities and nitrogenous
fertilizer which has a signicant role especially on
consisting green components in wheat production
by province group of enterprises the questionnaire
conducted (Table 4). This condition may also af-
fect the productivity in wheat production directly.
Table 4
Quantity of inputs used for wheat production
Indicators Provinces
Adana Ankara Diyarbakir Edirne Konya
Seed quantity (kg ha-1) 295.74 226.47 213.89 248.56 252.10
Std. dev. 40.59 35.39 24.39 34.26 38.07
C.V. 13.72 15.63 11.40 13.78 15.10
Nitrogen quantity (kg ha-1) 156.21 81.56 159.37 123.36 95.24
Std. dev. 65.18 24.97 27.63 32.03 48.10
C.V. 41.73 30.62 17.34 25.96 50.50
Phosphour quantity (kg ha-1) 58.15 63.75 52.20 66.72 67.35
Std. dev. 28.19 20.96 21.76 27.66 25.09
C.V. 48.48 32.88 41.69 41.46 37.25
Pesticide quantity (cc ha-1) 711.46 1243.26 845.42 564.83 1218.32
Std. dev. 645.00 602.09 851.50 311.00 638.08
C.V. 90.66 48.43 100.72 55.06 52.37
The Production Functions of Wheat Production in Turkey 247
The conducted research has proved that the seed
use level may vary between 220 and 260 kg ha-1 in
wheat production in Turkey’s condition (Ozcelik,
1989; Sade et al., 1999; Gundogmus, 1998). It has
been understood that when the ndings belonging
to analyzed enterprises and previous research nd-
ings are compared, the seed use quantity per unit is
high only in Adana province.
According to a research conducted in USA it
has been stated that the necessary pure nitrogenous
quantity may vary between 3 to 5 kg for every
100 kg grain (Halvarson et al., 1987) A research
conducted in Turkey has proved that wheat need
respectively 150 kg ha-1 and 160 kg ha-1 of pure
nitrogenous (Eker and Cagatay, 1999; Ozturk and
Gokkus, 2008). It has been observed that pure ni-
trogenous fertilizer use level in wheat production
is at the recommended level when the ndings be-
longing to the analyzed enterprises and other re-
search ndings are compared.
Halvarson (1987) has stated that pure phospho-
rus quantity required for grain yield and vegetative
improvement of wheat may vary 2.5 to 4 kg for
100 kg grain. A research conducted in Turkey has
recommended that 2 kg P2O5 should be given pure-
ly for 100 kg grain (Sencar et al., 1991). The pure
phosphorus use level per unit area in wheat pro-
duction is within the recommended level, as in the
nitrogenous fertilizer, in the analyzed enterprises,
According to two different studies which input
use level has been determined in wheat produc-
tion, it has been determined, that the pesticide use
quantities are respectively 1690 cc ha-1 and 2000
cc ha-1 (Gundogan, 1998; Ozcelik, 1989). In the
research, it has been concluded that pesticide use
level in the analyzed enterprises is below other re-
search ndings. That the upper limit of pesticide
in today’s wheat agriculture is on the level of 10
gr ha-1 and extensive uses of such kind of pesti-
cide give a certain idea of the average pesticide use
quantity, determined as a result of the research, is
below the other research ndings. The differences
and importance level by provinces in the aspect of
input use quantity per unit area in wheat produc-
tion have been stated in Table 5.
It has been determined that there is statistically
difference in point of seed use quantity per area
amongst the provinces analyzed in this research
except the difference between Diyarbakir and
Konya provinces. It has also been determined that
there is statistically difference in use level of pure
nitrogenous fertilizer in wheat production amongst
the other provinces except the difference between
Adana and Edirne provinces. It has been concluded
that there is statistically difference amongst Adana
– Diyarbakir – Konya provinces, Ankara – Edirne
and Edirne Diyarbakir and Konya provinces in
point of pure phosphorous fertilizer use level. It
has been determined that there is statistically dif-
ference in the amount of pesticide use amongst the
other provinces in the research except the differ-
ence amongst Adana – Edirne – Diyarbakir prov-
inces and Ankara – Konya provinces.
Functional Analysis of Wheat Production
Cobb – Douglas production function is one
of the most commonly used functions used in de-
termining resource use efciency in agricultural
production. The estimating equation of produc-
tion function relating to wheat production in this
research is given below:
Log Y= 0.235 + 0.507 Log X1 - 0.172 Log X2 +
0.494 Log X3 + 0.031 Log X4 + 0.228 Log X5 +
0.224 Log X6
Multiple determination of coefcient (R2) is
0.825 in the estimating equation and value of the
function “Fcalculation” is different from zero at 5%
signicance level (Fcalculation: 816.56 > Ftable: 2.09). All of
the variables in wheat production equation can
explain 82.5 % of changes in wheat production.
When the multiple determination of coefcients
obtained from other conducted researches relating
to this topic are analysed, it is seen that multiple
A. Semerci, A. Mazid, K. N. Amegbeto, M. Keser, A. Morgounov,
248 K. Peker, A. Bagci, M. Akin, M. Kucukcongar, M. Kan, S. Karabak, A. Altikat and S. Yaktubay
Table 5
Multiple comparisons of the wheat production factors by provinces
Dependent Variables (I) Provinces (J) Provinces Mean
Difference (I-J) Std. Error Sig.
Seed quantity (kg ha-1)
Adana
Ankara 69.269 4.121 0.000
Edirne 81.842 4.270 0.000
Diyarbakir 47.172 3.904 0.000
Konya 43.632 3.376 0.000
Ankara
Edirne 12.574 4.303 0.029
Diyarbakir -22.097 3.939 0.000
Konya -25.637 3.417 0.000
Edirne Diyarbakir -34.671 4.095 0.000
Konya -38.210 3.596 0.000
Diyarbakir Konya -3.540 3.152 0.794
Nitrogen quantity (kg ha-1)
Adana
Ankara 74.653 5.037 0.000
Edirne -3.161 5.219 0.974
Diyarbakir 32.857 4.771 0.000
Konya 60.973 4.126 0.000
Ankara
Edirne -77.814 5.259 0.000
Diyarbakir -41.796 4.815 0.000
Konya -13.680 4.177 0.010
Edirne Diyarbakir 36.018 5.005 0.000
Konya 64.134 4.395 0.000
Diyarbakir Konya 28.116 3.852 0.000
Phosphour quantity (kg ha-1)
Adana
Ankara -5.592 2.878 0.295
Edirne 5.949 2.983 0.269
Diyarbakir -8.569 2.727 0.015
Konya -9.198 2.358 0.001
Ankara
Edirne 11.541 3.005 0.001
Diyarbakir -2.977 2.751 0.816
Konya -3.606 2.387 0.556
Edirne Diyarbakir -14.517 2.860 0.000
Konya -15.147 2.511 0.000
Diyarbakir Konya -0.630 2.201 0.999
Pesticide quantity (cc ha-1)
Adana
Ankara -531.795 71.165 0.000
Edirne -133.955 73.742 0.364
Diyarbakir 146.635 67.411 0.190
Konya -506.863 58.302 0.000
Ankara
Edirne 397.840 74.302 0.000
Diyarbakir 678.430 68.024 0.000
Konya 24.933 59.010 0.993
Edirne Diyarbakir 280.590 70.715 0.001
Konya -372.907 62.092 0.000
Diyarbakir Konya -653.497 54.424 0.000
The Production Functions of Wheat Production in Turkey 249
determination coefcient of estimating equation
which is calculated in research is sufcient for
cross sectional data (Miran et al., 2002).
In this research, it has been utilized from “DW
(d) Test” for examining the autocorrelation in the
function. In the equation, “DW (d) Statistic” value
is calculated as 1.836 (K=6; n=781). In this study,
“DW (d) Statistic” calculation value has been com-
pared with table value and it has been concluded
that there is not negative or positive correlation in
model at 1% signicance level (dtable L: 1.613 U: 1.735).
In this research, “Student-t Test” method has been
applied in order to determine whether there is cor-
relation or not amongst the variables and partial
regression coefcient of the variables have been
calculated. Production elasticity coefcients, par-
tial correlation coefcients and signicance level
of the variables relating to wheat production func-
tion have been shown at Table 6.
All of the variables in estimating equation, ex-
cept the pesticide cost variable, are statistically
signicant at 1% level. When the values at table
6 are analyzed, it has been concluded that there is
not multicollinearity amongst the variables as the
determination coefcient is higher, the signicance
level of the explanatory variables is below 5% and
also partial correlation coefcients are lower (Gu-
jarati, 2009). Correlation coefcients showing the
relations amongst the factors have been given at
Table 7.
When the values relating to correlation coef-
cients are analyzed, it can be concluded that there
are relations especially amongst production area
(X1), seed (X2) and fertilizer cost (X3). This leads
to being careful while making marginal analysis
and economic interpretations (Zoral, 1973). Nev-
ertheless, unless multicolinearity does not has an
important effect on coefcient estimation, least
squares estimation can lose its integrity to some
extent but in such situations, the existence of the
multicolinearity can be ignored to some extent
(Ozcelik, 1994).
Table 6
Parameters and test values of wheat production function
Variables Elast. Coeff.
(βi) Std. Err. Partial Corr. “t- value” Sig.
X1 Production area (ha-1) 0.507 0.008 0.234 7.78 0.001
X2 Seed cost (€) -0.172 0.062 -0.097 -3.13 0.001
X3 Fertilizer cost (€) 0.494 0.046 0.349 12.02 0.001
X4 Pesticide cost (€) 0.031 0.018 0.043 1.39 0.164
X5 Precipitation (mm) 0.228 0.055 0.391 13.7 0.001
X6 Dummy variable 0.244 0.017 0.443 15.95 0.001
R2 : 0.825 F: 816.56 DW: 1.836
Table 7
Correlation matrix amongst the factors in wheat production
Prod. Quantity Prod. Area Seed Cost Fertilizer Cost Pest. Cost
X1 Production area 0.805(*)
X2 Seed cost 0.775 (*) 0.970(*)
X3 Fertilizer cost 0.873(*) 0.926(*) 0.903(*)
X4 Pesticide cost 0.703(*) 0.721(*) 0.702(*) 0.751(*)
X5 Precipitation 0.204(*) -0.034 -0.073(*) 0.082(*) 0.316(*)
(*) Signiciant at 5% level.
A. Semerci, A. Mazid, K. N. Amegbeto, M. Keser, A. Morgounov,
250 K. Peker, A. Bagci, M. Akin, M. Kucukcongar, M. Kan, S. Karabak, A. Altikat and S. Yaktubay
The sum of the production coefcients of the
factors in wheat production estimating equation,
when the dummy variable is excluded, has been
calculated as (∑βi) 1.089. This value can be inter-
preted, as 10% increase in the inputs will lead to
10.89% increase in wheat production amount on
condition that the combination of the independent
variables remains stable.
When the production elasticity of the variables
in the estimating equation is examined, it is under-
stood that only the coefcient of seed cost factor
(X2) has negative character. The highest marginal
production elasticity coefcient belongs to produc-
tion area (X1) amongst the variables in estimating
equation. Production elasticity coefcients means
a percentage increase rate to which a 1% increase
in input will lead in wheat production. For instance,
10% increase in fertilizer cost (X3) which is one of
the variables in the production equation will lead
to 4.94 % increase in wheat production. Dummy
variable factor (X6) in equation function, given at
Table 6, is signicant at 1% level and it has been
clearly understood that production in irrigated con-
ditions out-tops the production in dry conditions
i 0.244). Marginal yield, marginal income, factor
prices and marginal efciency coefcients of the
variables in the equation have been given at Table
8. Calculations of the marginal yield, marginal in-
come and marginal efciency coefcients relating
to factors in wheat production have been given at
method part. In this research, crop price has been
taken as 0.27€ kg-1 in calculation of marginal ef-
ciency of coefcient.
The highest marginal yield value amongst the
factors in estimating equation belongs to produc-
tion area (X1). On condition that use levels of the
other inputs remain unchanged, one unit increase
in production leads to 140.91 kg increase in pro-
duction quantity. On condition that production
inputs remain stable, one unit increase in precipi-
tation leads to 12.73 kg increase and respectively
pesticide 11.81 kg and fertilizer 10.12 kg increase
in wheat production quantity.
According to marginal yield values of inputs
used in wheat production, one unit increase in
production area (X1) leads to 38.05€ increase in
wheat income and respectively 3.44€ increase in
precipitation (X5), 3.19€ increase in pesticide (X4)
and 2.73€ increase in fertilizer factor (X3).
While elasticity coefcients’ marks of the pro-
duction functions give information about use cases
of the relevant factors, it may be said that efcien-
Table 8
Marginal income, marginal yield and marginal efciency coefcients of the variables in wheat production
Variables Geo. Mean Marg.Prod.
Elast. Marg.Yield, kg Marg. Income
(€)
Factor Price
(€)
Marg. Efc.
Coeff.
Y Production
quantity (kg) 20008.75 -----
X1 Production area
(ha-1) 71.99 0.507 140.91 38.05 23.00 1.65
X2 Seed cost (€) 649.53 -0.172 ----
X3 Fertilizer
cost (€) 976.63 0.494 10.12 2.73 0.41 6.66
X4 Pesticide
cost (€) 52.52 0.031 11.81 3.19 2.50 1.28
X5 Precipitation
(mm) 358.35 0.228 12.73 3.44 - -
The Production Functions of Wheat Production in Turkey 251
cy coefcients give more clear and explicit infor-
mation about the use cases of the factors (Akcay
and Uzunoz, 1999). As it is known, using factors
at optimum level is very important in terms of in-
creasing productivity and protability in produc-
tion. However, use of resources at optimum level
in any production process is possible when the in-
puts are used equally with the price or opportunity
cost of the marginal yield value (Henderson and
Quandt, 1971).
When the marginal efciency coefcients of the
production factors in the wheat production equa-
tion are analyzed, it has been concluded that there
must be an increase in these inputs in order to be
able to use production area (X1), pesticide (X4) and
fertilizer cost (X3) variables at optimum economi-
cal level as the marginal efciency coefcients,
reached by division of marginal crop income to
factor price, are greater than 1.
Conclusion
Nowadays, efcient use of resources has been
one of the most signicant terms in any production
branch. In recent times, it is observed that there
has been an increase in the studies about determin-
ing efciency level of inputs that are used in agri-
cultural activities in terms of agricultural produc-
tion. In this conducted research relating to wheat
production, one of the chief products for human
nutrition, the relations between the inputs used in
production and production quantity have been ana-
lyzed in Turkey.
In this research, Cobb – Douglas production
function has been applied while determining the
resource use level as it is in many other studies
relating to agricultural production economics. Ex-
cept the pesticide factor, the factors in the estimat-
ing equation which is created by means of data ob-
tained from the enterprises analyzed in the scope
of research have been considered as signicant
in terms of statistics. It has been understood that
only the seed cost variable in the equation have
negatively affected the production quantity. There-
fore, it needs to be taken into consideration that the
seed quantity that will be used in wheat produc-
tion must be in the recommended quantity accord-
ing to regions by various research institutions and
agricultural faculties. In wheat production, there
is increasing return to scale in respect to sum of
elasticity coefcients of the factors in estimating
equation.
In this research, it has been concluded that fer-
tilizer and pesticide costs should also be increased
besides production area in order to make increase
in wheat production according to efciency coef-
cients of production factors. In addition to this,
at the end of the research, it has been proved that
there are signicant differences in terms of input
use in wheat production amongst the provinces in
different regions of Turkey and this situation af-
fects wheat yield. In this study it has been deter-
mined that there are statistical differences in terms
wheat yield amongst the provinces analyzed in this
research. The differences amongst the provinces in
terms of wheat yield cause to enterprises benet
from agricultural support payments (both product
quantity and area based supports) at different rates.
This leads to differences amongst the provinces
and regions in terms of wheat cost and producer
income.
In this research, it has been concluded that input
use should be in the recommended amount, pro-
duction should be made in irrigated areas, subsidy
payments and other subsidies which are given for
promoting wheat production should be determined
in reel term, and payments should be made on
time in order to Turkey able to compete with other
countries in wheat production.
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... Many functional forms have been defined in the literature for the analytic study of production process (Griffin et al., 1987;Semerci et al., 2012). However, economic theory provides mainly generic conditions of specification and provides little guidance for specifying a function. ...
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