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Turkish Journal of Agriculture - Food Science and Technology, 7(11): 2012-2017, 2019 DOI Turkish Journal of Agriculture - Food Science and Technology, 7(11): 2012-2017, 2019 DOI: https://doi.org/10.24925/turjaf.v7i11.2012-2017.2998 Turkish Journal of Agriculture - Food Science and Technology Available online, ISSN: 2148-127X | www.agrifoodscience.com | Turkish Science and Technology Input Use Efficiency in Sunflower Production; A Case Study of Konya Province (Karatay District) Cennet Oğuz1,a, Aysun Yener Ögür1,b,*, Aycan Ayhan1,c 1Department of Agricultural Economics, Faculty of Agriculture, Selçuk University, 42250 Konya, Turkey *Corresponding author A R T I C L E I N F O A B S T R A C T The aim of the study is to analyse the efficiency of input use in sunflower production in Karatay Research Article district. Turkey ranks 10th in sunflower production in the world and Konya province has 13.39% capacity of sunflower production in Turkey, placing the 2nd place in terms of production. 97% of sunflower produced in Konya province is sunflower for oil. Data used in this study was determined Received : 24/09/2019 as 51 enterprises manufacturing sunflower according to the Stratified Sampling Method. The Accepted : 24/10/2019 economic efficiency results of sunflower production were calculated with Data Envelopment Analysis (DEAP) method. The total Gross Production Values (GPV) obtained from the enterprises is 50,221.43 $, the GPV acquired from the sunflower production is 23,844.70 $, the total gross profit is 36,927.29 $ and the sunflower gross profit is 18,285.16 $. According to the efficiency results, Keywords: economic efficiency was found to be 0.604, resource efficiency was 0.604, technical efficiency was DEAP 0.868, and pure efficiency was 0.922 while scale efficiency was 0.942. It should be ensured that Economic analysis enterprises use their resources effectively. Information meetings should be held for enterprises on Konya Sunflower production resource use. Regression analysis a b [email protected] https://orcid.org/0000-0002-2764-0759 [email protected] https://orcid.org/0000-0001-7846-4866 c [email protected] https://orcid.org/0000-0003-3338-0153 This work is licensed under Creative Commons Attribution 4.0 International License Introduction One of the most important food sources vital for human Turkey is 1,964 thousand tons. 8.37% of total sunflower is oil. Oils are divided into two categories as vegetable oils production is for snacking. Due to the increasing and animal oils. Vegetable oils are obtained from different population and per capita consumption, the increasing oil agricultural products in different countries. In the world, deficit is met by imports. Thus, sunflower production is oilseed crops are soybean, peanut, sunflower, rapeseed, rather pivotal for Turkey. Konya province constitutes corn, olive, sesame, palm kernel, cotton, flaxseed, 14,02% of the total sunflower production (210,307 ton), safflower and castor oil (Semerci, 2014). Oilseed plants are and 8.09% of the production area (460,376 decare) in defined as essential necessities that are quite vital in human Turkey. Karatay district constitutes 38.79% sunflower nutrition (Boland, et al. 2001). The habit of vegetable oil production and 33.46% of the production area of Konya consumption varies from one country to another. For (TSI, 2019). The oil shortage is important for Turkey, the instance, the most important source of vegetable oil in the efficiency in production and increasing the operational US and Italy is soybeans while it is sunflowers in Turkey efficiency of resource use must be ensured for this (Semerci, 2014; Berk, 2017). shortage. The main purpose of enterprises that make The total amount of sunflower production in world is productions in both agricultural and non-agricultural 47,863 thousand tons (FAO, 2019). Turkey’s share in sectors is to reduce the cost. The best-known way to reduce world sunflower production is 3.53%. Sunflower field in costs is to increase production efficiency. Turkey takes the first place with 550-650 thousand For this aim, economic activity results of the hectares. The total amount of sunflower production in enterprises, factors affecting the efficiency and input use Oğuz et al. / Turkish Journal of Agriculture - Food Science and Technology, 7(11): 2012-2017, 2019 efficiency of the enterprises were calculated in the Karatay In the study, the efficiency analysis of sunflower district, constituting the majority of the sunflower producing enterprises was conducted according to Data production. Sunflower production was examined based on Envelopment Analysis (DEA). The Data Envelopment enterprises and scale analysis was conducted. Additionally, Analysis (DEA) of nonparametric methods measures the the amount of inputs used in production, cost and relative efficiencies of n number decision-making unit. This efficiency analyses were carried out according to the method is used to measure the performance of decision units sources of efficiencies. that are similar to each other by determining the weights of inputs and outputs in production relations with multiple Material and Method inputs and multiple outputs (Coelli, 1996). In addition, it reveals efficiency scores that express the relationship The main material of the study is composed of the between DEA and input-output and enables the adjustment original data collected from the surveys of the enterprises of inputs and outputs for more efficient use of resources. It producing sunflower in the year 2015 in Karatay district of distinguishes between efficient and inefficient enterprises Konya Province, which was selected as the research region. and designates the most suitable reference set for inefficient In addition, the data that the relevant institutions and enterprises to become effective (Güngör and Demirgil, organizations previously gathered on the subject was also 2005; Tosun and Aktan, 2010). Technical activity in DEA is benefitted. divided into two as pure technical efficiency and scale In the study, stratified sampling method, was utilized in efficiency. Measurement of the pure technical efficiency is order to increase the accuracy of the data collected from calculated by efficiency limit estimation under the the enterprises and to provide sufficient representation of assumption of variable return according to the scale. The ratio of technical efficiency to pure technical efficiency different sections in the population (Yamane, 1967; Güneş indicates the scale efficiency (Kumar and Gulati, 2008; and Arıkan, 1988). Layer widths are grouped to 1-50 Coelli et al., 1998). There are Charnes-Cooper-Rhodes decares (one tenth of a hectare), 51-150 decares, 151 (CCR) and Banker-Chaenes-Cooper (BCC) models in DEA. decares and above. Sample volume was calculated as 51% Since the farmers are in a position to control the inputs more according to the Neyman Method with a margin of error of than the outputs, Farrell's efficiency measurements for input 5% at a confidence interval of 99% (Yamane, 1967). were used in this study. A multi-input-single output model was created for business groups. Economic efficiency of the ∑ (Nh.Sh)2 n= input for each individual business was obtained with the N2.D2+ ∑ Nh .(Sh)2 solution of the following linear programming model: The formula description is as below; d 2 D2=( ) Min λ W ×X , t xi i i Kısıt-yi+yλ ≥0 n = Number of samples, θxi-Xλ≥0 λ≥0 N = Number of enterprises in the population, N = Number of enterprises in h layer, h Wi = Vector of input prices for the enterprise in the I S = Variance of h layer, h ranking d = The margin of error allowed from the population Xi = Vector of input-quantity-cost minimization average, calculated for the enterprise in the I ranking, t = t table value corresponding to the 99% Yi = output level confidence limit predicted in the study. Λ = constant vector The distribution of the sample volume according to the The efficiency value for the enterprises in the ranking of enterprise groups is given in Table 1. Xi×i is between 0 and 1. The fact that Xi value is equal to 1 indicates that enterprises have technical efficiency. For Table 1 Sample Volume by Enterprise Size Groups in the inefficient enterprises, on the other hand, the value of Xi is Research Field (n) less than 1 (Coelli, 1998). Banker et al. (1984) developed Enterprise Size Groups (decare) Sample Size (n) Data Envelopment Analysis (DEA) model, which is based 0-50 (1st Group) 7 on Constant Return to Scale (CRS) assumption, in the way 51-150 (2nd Group) 9 to take Variable Return to Scale (VRS) into consideration 151-+ (3rd Group) 35 and this model is known as BCC. Indeed, if none of the Total 51 production units operates at the optimal scale, the use of the constant return definition according to the scale results in a In the research area, if the enterprises gain 30% or more measure of technical efficiency mixed with scale of their income from sunflower, these enterprises are efficiencies. Therefore, a limiting factor (N1’λ=1) providing defined as “the enterprises that produce sunflower” (Oğuz, convex to CRS model, was added and the model was 1991). Various literatures were used in the calculation of transformed into a VRS model. Because the addition of this economic activity results of enterprises (Oğuz and Altıntaş, limitation to the model hinders the calculation of scale 2002; Semerci, 2014; Oğuz and Bayramoğlu, 2018). In this efficiency, the minimum cost in CRS conditions was study, 1 US$ = 2.56 Turkish Liras calculated calculated by proportioning the minimum cost to the VRS (approximately, in Temmuz 2015). conditions when calculating the scale efficiency. DEA was conducted in DEAP 2.1 package program. 2013 Oğuz et al. / Turkish Journal of Agriculture - Food Science and Technology, 7(11): 2012-2017, 2019 Results and Discussion is 50,221.43 $ according to table 4. In the first group of the enterprise, the value was determined as 6,708.42 $, Socio-economic conditions of the enterprises in the 19,487.09 $ in the second group and 66,827.14 $ in the research area are given in Table 2.
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