Maize is a plant that has been beneficial for people since ancient times. It
originated from America and was distributed worldwide following the European
colonization of America. It is accepted that maize was introduced to Turkey
through North Africa, Syria and Egypt in the 1600s. Its use varies widely
and, in addition to use as a human food, it is also used as animal feed. It
is an important raw material for several industries, including starch, vegetable
oil and paper. Maize hybrids can grow between 60 and 42 South meridians (Kirtok,
People living in developing countries can not reach sufficient food sources
originated from animals. It causes malnutrition in those countries. In this
case, people tend towards to vegetable-origin proteins such as corn. Because
it provides evenly same protein content. Also it supply cheaper protein source
than animal sources and it can be found in everywhere (Adeniji,
2008). Of the global maize production, 27% is used in human nutrition and
73% for animal feed. Maize is the third most extensively planted crop, after
wheat and paddy. Due to the large yield per area, maize provides the largest
harvest. Maize is grown more than 100.000 tons in 101 countries in the world
it has the highest average grain harvest in the world (FAO,
ABD, China, Brazil, Mexico, France account for a significant proportion of
maize production. Turkey has 0.4% of the harvested maize area, providing approximately
0.5% of global maize production (FAO, 2008).
In Turkey, maize is the third most important grain crop in terms of plantation
size and harvest yield, after wheat and barley; in 2005, 3.9% of the total plantation
area and 6.8% of the total grain harvest were estimated to be maize (Turkstat,
East Mediterranean Region produces almost half of Turkeys maize production
(42-48%). Adana province has the highest maize production capacity in Turkey,
followed by Sakarya, Mersin, Samsun and Osmaniye (Turkstat,
As in other countries, agricultural production in Turkey is supported or interventions
are made in agricultural markets for a number of reasons, including increasing
production, quality and efficiency of agricultural products; broadening the
range of agricultural production; protecting producers and consumers and ensuring
food safety and sustainable development. To this end, various political tools
are utilized, most commonly market price support, input price subsidies, making
direct payments and providing research, publication, control, infrastructure
services and general services (Yeni and Dolekoglu, 2003;
Tastan, 2005). While, these political tools are utilized
directly or indirectly in Turkey, the most frequently policy measure is market
In Turkey, maize policy was introduced in 1941 as a result of the appointment
of the Turkish Grain Board (TMO). The Turkish Grain Board (TMO) regulated and
supported the maize market for many years by purchasing maize. However, in the
2006/2007 marketing year, no maize purchase price was announced and no support
purchases were made by TMO, as supply-demand balance was formed in the domestic
market and the price was high. However, the act of providing credit to maize
production is maintained by the Ministry of Agriculture and Public Affairs (Tasdan,
2007). The Turkish Grain Board paid 459 TL ton-1 of maize during
the 2009-2010 purchase periods. The Ministry of Agriculture and Public Affairs
made a premium payment of 40 TL ton-1 for the grain maize.
Some researchers have previously estimated the technical efficiency of maize
production. Kibaara (2005) estimated the level of technical
efficiency in maize production in Kenya using the Stochastic Frontier Approach.
The results indicated that the mean technical efficiency of Kenyas maize
production is 49%; however, this ranges from 8 to 98%. There is distinct intra
and interregional variability in technical efficiency in the maize producing
regions. The number of years that the farmer attended formal education, age
of the household head, health of the household head, gender of the household,
use or non-use of tractors and off-farm income all affect technical efficiency.
Awudu and Eberlin (2001) used a translog stochastic
frontier model to examine technical efficiency in maize and bean production
in Nicaragua. The average efficiency levels were 69.8 and 74.2% for maize and
beans, respectively. In addition, the level of schooling (representing human
capital), farming experience (represented by age) and access to formal credit
contributed positively to production efficiency, while farmers participation
in off-farm employment tended to reduce production efficiency. Large families
appeared to be more efficient than small families. A positive correlation was
identified between inefficiency and participation in non-farm employment, which
suggests that farmers reallocate time away from farm-related activities, such
as adoption of new technologies and gathering of technical information that
is essential for enhancing production efficiency.
Seyoum et al. (1998) investigate the technical
efficiency and productivity of maize producers in Ethiopia and compared the
performance of farmers within a program of technology demonstration with that
of non-participating farmers. Using Cobb-Douglas stochastic production functions,
their empirical results show that farmers that participate in the program are
more technically efficient, with a mean technical efficiency score of 94%, compared
with those outside the project, who had a mean efficiency score of 79%.
Chirwa (2003) estimated technical efficiency among smallholder
maize farmers in Malawi and identified sources of inefficiency using plot-level
data. It was found that smallholder maize farmers in Malawi were inefficient;
with an average efficiency score of 53.11 and 58% of the plots have efficiency
scores below 60%. The results of the study reveal that inefficiency declines
with plot size; on plots that used hired labor; on plots that use hybrid seeds;
and with membership to a farmers club or association.
Although, a considerable number of studies were conducted on maize production
and costs, most previous studies focused on farm budget analysis, production
cost, policy and market (Akdemir et al., 1994;
Gul et al., 1995; Gul and
Orhan, 1998; Aktas and Oguz, 2004; Tastan,
2005). In contrast, the present study adopts a management perspective.
The objective of the present study is the measurement of technical and scale
efficiency of sec crop maize growing farms in Çukurova region in Turkey.
To this end, a modified input oriented DEA approach is applied to 89 farms located
in the Çukurova region of Adana and Hatay provinces. The objective of
this study is also to inform future policy decisions on improving sec crop maize
farming efficiencies, by revealing and explaining variations in technical efficiencies
of sec crop maize growing farms and determining the causes of inefficiencies.
MATERIALS AND METHODS
The data used in this study was collected through a questionnaire study from
second crop maize growing farmers in two provinces of Turkey. These provinces
(Adana and Hatay) account for approximately 65% of East Mediterranean Regions
maize harvested area and production (Turkstat, 2008).
The survey provides detailed cross-sectional information on revenues and production
costs for the surveyed farms during the 2004-2005 production period. Sample
farms were selected with a stratified sampling procedure. A total of 89 sec
crop maize growing farms were interviewed for the analysis. Farm groups and
interviewed farm numbers are given in Fig. 1.
The study used the Data Envelopment Analysis (DEA) method. There are two general
approaches to measuring technical efficiency, parametric and non-parametric
methods. Data Envelopment Analysis (DEA) is a non-parametric method and can
easily handle multiple input and output cases.
|| Farm groups and interviewed farm numbers
Moreover, in DEA, application inputs and outputs can have very different units
of measurement without requiring any a priori tradeoffs or any input and output
prices. Given these highly desirable features of non-parametric methods, it
is not surprising that they have become very popular (Fousekis
et al., 2001).
An input oriented BCC (Banker-Charnes-Cooper model) model is given below for
N Decision Making Units (DMU), each producing M outputs by using K different
inputs (Coelli et al., 1998; Dagistan
et al., 2009; Gul et al., 2009):
where θ is a scalar, N1 is convexity constraint and λ is Nx1
vector of constants. Y represents output matrix and X represents the input matrix.
The value of θ will be the efficiency score for the ith firm. This linear
programming problem must be solved N times, once for each firm in the sample.
A θ value of 1 indicates that the firm is technically efficient according
to the Farrell (1957) definition.
A ratio of technical efficiency scores obtained from DEA under CRS (Constant
Return to Scale) and VRS (Variable Return to Scale) assumptions measures the
Scale Efficiency (SE). This scale efficiency measure can be interpreted as the
ratio of the average product of a firm operating at a point to the average product
of another firm operating at a point of technically optimal scale. A value of
scale efficiency equal to 1 implies that the farm is scale efficient and a value
less than 1 suggests the farm is scale inefficient. A farm operating under decreasing
returns to scale conditions means that it is operating under super-optimal conditions.
On the other hand, a farm operating under increasing returns to scale is operating
under sub-optimal conditions (Dagistan et al., 2009;
Gul et al., 2009).
An input oriented DEA model was chosen and one output and five inputs were
used in the DEA model. These are:
||Output (Y): Second crop maize yield per unit area (kg
||Inputs: (X1) pure nitrogen applied to unit area (kg ha-1)
||(X2) pure phosphorus applied to unit area (kg ha-1)
||(X3) amount of seed used in sec crop maize unit area (kg ha-1)
||(X4) total labor used (h ha-1) in maize farming from land
preparation through harvest (both family and hired labor)
||(X5) total machinery working hours (h ha-1)
DEA scores were estimated using the DEAP software (version 2.1), developed
by Coelli (1996). Efficiency scores of the farms were
calculated under constant and variable return to scale assumptions (CRS and
VRS). The Tobit regression model was used to determine causes of inefficiencies
after calculating DEA scores. Several socio-economic factors were regressed
upon DEA VRS scores in this model. Farmer age, education level, maize harvesting
areas groups, number of irrigation, number of pesticide and off-farm income
Structural properties in second crop maize farming: East Mediterranean
Region consists of Adana, Hatay, Icel and Osmaniye provinces and has the highest
agricultural production in Turkey. Although it has only 5.3% of total agricultural
land area (1.4 million hectare), the region produces 8.82% of total agricultural
production value and 11.43% of total plant production value (SIS,
East Mediterranean region has a surface area of 4.06 million ha, of which 34.6%
(1.45 million ha) (SIS, 2003) is agricultural land. Agricultural
land, compared with 32.4% agricultural usage for Turkey. The principle agricultural
products of the region are wheat, cotton, citrus fruit, maize and groundnuts.
Approximately 39% of the agricultural land area is irrigated.
Some socio-economic indicators of maize farms are given Table
1, in the enterprises surveyed in the present study; the average age of
the maize growers was 46.54 years. There was no significant difference between
the Adana and Hatay farm groups with respect to growers age and education level.
The average duration of education was 7.29 years. The average family size in
the surveyed households was 4.55 people. There was no significant difference
between the farm groups with respect to demographic characteristics. Of this
family size, 53% were men and 47% were women (Table 1). The
major crops grown on the surveyed farms were wheat, maize and cotton.
Particularly after the 1960s, the increased use of water in agriculture
led to increases in harvest and production and allowed increased variety in
production. With the Sec Crop Project, implemented by the Turkish Ministry of
Agriculture in 1982, maize agriculture in the region increased while cotton
plantations decreased. Most of the producers who gave up cotton production started
to grow maize and soya beans. In the fields under observation, the number of
maize plantation fields has increased since 1985. The region has a production
advantage compared to other regions in terms of the use of good quality seed.
The study results showed that growers use chemical fertilizer 2.02 times during
the production period. Nitrogen fertilizer (250.48 kg ha-1) and P
fertilizer (28.32 kg ha-1) were applied during June, July and August.
Most maize land in the region is irrigated by irrigation channels. In the surveyed
area, the frequency of irrigation was 5.6 times during the maize production
period. Irrigation was generally used from June through August.
|| Socio-economic characteristics of farmers growing second
||Summary statistics for variables used in the efficiency analysis
It was observed that spraying for weeds was not common, but hoeing was a very
common treatment for weeds. The average number of pesticide applications was
When coefficients of variations are taken into consideration, it is clearly
seen from Table 2 that the greatest variations are in fertilizer,
seed and pesticide use. Those large variations may be an indicator of mismanagement
The research findings indicate that, in the region, there is much greater use
of fertilizers than the recommended amount. Also research organizations suggest
to use 180-240 kg ha-1 nitrogen (Anonymous, 1994).
It is seen that rates of fertilizer application vary greatly between producers.
Communicating the study findings to agricultural producers and providing information
about the appropriate application of fertilizers may affect the attitude of
the producers. In comparison, it can be seen from Table 2
that producers act more consciously in utilizing agricultural machinery, seed
and labor resources.
Technical efficiency of farms: The results of the input oriented DEA
analysis are given in Table 3. The results show that 8 farms
under CRS and 16 farms under VRS were found to be fully efficient. Two farms
under CRS showed a performance below 0.40. Predicted technical efficiencies
differ among sample farms, ranging between 0.41 and 1.00, with a mean technical
efficiency of 0.81 (Table 3). These results indicate that
there are some opportunities for improving resource-use efficiency. The surveyed
farms may reduce their inputs by an average of 19% while achieving the same
||Farm characteristics with respect to returns to scale
||Input slacks and number of farms using excess inputs
Considering the enterprise performing at minimum efficiency, the results suggest
that a saving of 39% will be achieved in input use on condition that this enterprise
becomes active. For the inefficient farms, the causes of inefficiency may be
either inappropriate scale or misallocation of resources. Inappropriate scale
suggests that the farm is not taking advantage of economies of scale, while
misallocation of resources refers to inefficient input combinations. Since,
mean scale efficiency of the sample farms is relatively high (0.88), it can
be concluded that inefficiencies are mainly due to improper use of inputs rather
than inappropriate scale.
The mean scale efficiency of the surveyed sec crop maize farms is 0.81. Of
the 89 sec crop maize enterprises, 8 show constant returns to scale, 78 show
increasing returns to scale. There are 3 farms practicing under decreasing returns
to scale conditions. The characteristics of optimal, sub-optimal and super-optimal
farms are given in Table 4. As seen from Table
4, there are great differences between maize yield per ha and mean gross
return per unit.
Excess use of inputs: Following the Data Envelopment Analysis, it was
found that enterprises functioned at an average efficiency level of 88%. That
is to say, it was determined that enterprises could reduce their inputs by an
average of 12% in the present condition.
It was found that 63 of the examined enterprises make excessive use of phosphor
(average level 33.38% excess), 50 enterprises make excessive use of machine
power (average 29.22%), 39 apply excessive nitrogen (average 22.58%), 28 employ
excess labor (average 32.17%) and 5 enterprises use seed excessively (average
0.23%) (Table 5).
Determinants of technical efficiency: VRS DEA technical efficiency scores
were regressed on farm specific characteristics in order to identify sources
of inefficiencies. Since, efficiency scores range between 0 and 1, a two-tailed
Tobit model was employed in place of OLS regression (Ray,
2004). The results of the Tobit regression analysis are given in Table
The area used for sec crop maize was found to have a positive effect on efficiency.
This parameter is statistically significant at the 5% level.
Number of pesticide application is expected to have an adverse effect on efficiency,
due to pesticide cost associated increases in the total cost and also increase
in machinery and labor use. As expected, this parameter was found to have a
negative effect, but the effect is not significant at the 5% level.
The number of irrigation applications also has a negative sign, as expected,
although this parameter is not significant at the 5% level.
|| Results of tobit model for efficiency scores
Farmers age is not statistically significant even at the 10% level. This
parameter has a positive sign.
Formal education of the farmer was found to have a negative effect on efficiency,
but this parameter is not statistically significant.
The relationship between education and efficiency is the topic of many scientific
studies conducted on efficiency. While, some researchers have reported that
the relationship between efficiency and education is positive, others found
that the relationship is negative or there is no relationship at all. Bravo-Ureta
and Evenson (1994) found, in a study conducted in Paraguay, that there was
no significant relationship between education and efficiency. In a study to
determine the efficiency of wheat producers in the Southeastern Anatolia Region
of Turkey, Alemdar and Oren (2006) determined that education
has a negative impact on technical efficiency.
In an efficiency study conducted on maize producers in Cameroon, Binam
et al. (2004) found that the level of formal education was not closely
related to efficiency. Kumbhakar et al. (1989)
reported a positive relationship between the educational status of dairy operators
in Utah and the efficiency of the enterprises. Similarly, Huang
and Kalirajan (1997) determined that the efficiency of enterprises increased
as a result of higher educational levels among managers of maize and rice enterprises
in China. In a study of the efficiency of dairy plants in England, Bravo-Ureta
and Rieger (1991) found a positive but non-significant relationship between
education and efficiency. It is remarkable in the examined studies that the
relationship between education and efficiency is not generally strong in the
developing countries. Off-farm income was found to have a positive effect on
efficiency, but this parameter is not significant at the 10% level.
This study aimed to provide estimates of the technical efficiency of sec crop
maize production enterprises in the East Mediterranean region of Turkey and
to explain variations in technical efficiency between farms. The results indicate
that mean technical efficiency is 81%. Therefore, there is a 19% scope for increasing
maize production by using the present technology. However, Technical Efficiency
(TE) ranges between 41 and 100% among the sec crop maize producers in East Mediterranean
region. The greatest excesses were observed in fertilizer, machinery and labor
use. All these excesses adversely affect technical efficiencies of second crop
maize farming. Inefficiencies indicate a sub-optimal use of these inputs. In
the light of these results, choosing a crop pattern appropriate for the soil
type and appropriate use of agricultural inputs is one of the topics to be highlighted
in terms of sustainability. In order to promote more sustainable forms of agriculture,
producers should be informed about the conservation of scarce sources and the
optimal use of chemical or resource-intensive inputs.
In similar studies carried out on this issue, the variation in technical efficiency
was found to be 50% (for instance; in corn enterprises operating in the Jimma
Zone region, in Southwest Ethiopia). Accordingly, specific differences were
detected between the enterprises in terms of their technical efficiency in access
to animal ownership, participation in additional programs and access to infrastructure.
Therefore, it was suggested that improving farmers (in Jimma region) access
to infrastructure can increase the technical efficiency of corn production (Yilma
and Berg, 2001). In corn enterprises operating in the Local Government Area
of Ogbomoso South in Oyo state (Nigeria), total variation due to technical inefficiency
was found to be 13% (Oyewo et al., 2009). The
technical efficiency of Himachal agricultural enterprises in the North West
Himalayan Region was calculated by using a frontier production function. Technical
inefficiency in corn production was calculated to be 35-42%. In addition, efficiency
in the use of seed, male workforce and the use of chemical and farm fertilizers
gradually increased. The study therefore emphasised that it was of the utmost
importance to train the female workforce in the principles of resource management
(Sharma et al., 2008). In corn enterprises operating
in the Chitwan region of Nepal, fertilizers represented the biggest share among
the production costs, followed by workmanship and tractor costs. Moreover, the
number of school years per household and corn production land per household
was found to have positive effects on the cost efficiency of the corn enterprises.
Scale impact analysis of the specialized corn production enterprises showed
that production increase drastically exceeded the total costs and, in turn,
yield increased according to the scale of the enterprise (Paudel
and Matsuoka, 2009).