A.1.4 Meat Science 95 (2013) 798–804 Contents lists available at SciVerse ScienceDirect Meat Science journal homepage: www.elsevier.com/locate/meatsci Policy implications of trends in Turkey's meat sector with respect to 2023 vision Fahri Yavuz ⁎, Abdulbaki Bilgic, Mustafa Terin, Irfan O. Guler Department of Agricultural Economics, Ataturk University, 25240, Erzurum, Turkey article info abstract Article history: Turkey has become one of the leading emerging economies in the world being second after China as the Received 14 February 2013 highes ecoonomically growing country with 8.9% economic growth rate in 2010. Forecasting impacts of Accepted 21 March 2013 this development in coming 10 years might have very important policy implications for the meat sector in the framework of 2013 vision of Turkey. In this study, annual time series data which contain several key vari- Keywords: ables of meat sector in last 26 years (1987–2012) are used to forecast the variables of the coming twelve Turkey years (2013–2024) to drive policy implications by considering the impacts of high economic growths, crises Meat industry 2023 vision and major policy changes. Forecasted future values of the variables for 2023 in the sector are assessed and com- Time series pared with recent national and international values to drive policy implications. The results show that the eco- Forecasting nomic growth results in the increase in per capita income and thus increased demand for meat seemed to Economic policy foster the meat sector. Therefore, these macroeconomic indicators need to be better in addition to improvements at micro level for establishing competitive meat sector and thus reaching aimed consumption level of meat. © 2013 Elsevier Ltd. All rights reserved. 1. Introduction hand, the above mentioned improvements in macroeconomics in the country are at the same time reflected in agricultural sector. Agricul- In the last decade (2001–2011), Turkey has become one of the tural income increased from 23.7 to 62.0 billion dollars, agricultural leading emerging economies in the world being second after China supports increased by 255%, credits provided to farmers were 30 as the highest economically growing country with 8.9 percent growth times higher with an interest rate of 5% which used to be 59% and rate in 2010. Turkey's GDP growth rate for the first quarter of 2011 Turkey's agriculture placed first and seventh among European coun- reached a record rate of 11% year-on-year, according to the data re- tries and in the world, respectively, in terms of agricultural production leased by the Turkish Statistical Institute (TurkStat, 2012). Surpassing value. market estimates of around 9.6%, Turkey has become the fastest In order to reach the “goal 2023” which is the mission of the Turkish growing country in the world for the first quarter of 2011, followed economy to head among ten big economies by the year 2023, the 100th by Argentina at 9.9% and China at 9.7%. anniversary of Turkish Republic, Turkey's agriculture needs to perform These figures are the outcomes of overall developments related to better to reach that goal by solving its major problems. Solving these Turkey's economy in the last decade. Loan to IMF decreased from 25.6 problems could let agricultural sector to be in the first five countries to 2.3 billion dollars, inflation rate declined from 68 to 4.8%, annual ex- by having 150 billion dollars agricultural income, 40 billion dollars ag- port values increased from 25 to 114 billion dollars, per capita income ricultural export, and all the lands to be irrigated. Turkey's meat sector increased from 3000 to slightly less than 11,000 dollars, reserves in has to be a part of this progress. central bank increased from 22 to 82 billion dollars, two line intercity Given the shortage in red meat consumption, it is clear that the roads increased from 6100 to 19,700 km, budget for health and educa- remarkable rapid growth rate in Turkey fosters per capita income, tion increased about five times and public investment increased from resulting in increased demand for meat. These are the reasons for 8.7 to 35 billion dollars. the apparent excessive prices in red meat in recent years. Forecasting Turkey's rapid economic development coupled with political sta- impacts of these developments in the coming 10 years might have bility in the last decade has also brought about significant changes in very important policy implications for meat sector in the framework both consumers' and producers' behaviors. Rapid urbanization and of 2013 vision of Turkey. In this study, regional and annual time series women in labor pool have started diverting the dietary patterns across data which contain several variables of meat sector in the last 24 years the nation towards more processed and pre-prepared foods in re- (1987–2012) will be used to forecast the variables of the coming sponse to long working hours and less physical activity. On the other twelve years (2013–2024) to drive policy implications by considering the impacts of high economic growths, and crises and major policy changes. Forecasted future values of the variables for 2023 in the ⁎ Corresponding author. Tel.: +90 442 231 1481; fax: +90 442 231 2678. E-mail addresses: [email protected] (F. Yavuz), [email protected] (A. Bilgic), sector will be assessed and compared with recent national and inter- [email protected] (M. Terin), [email protected] (I.O. Guler). national values to drive policy implications. As long as the current 0309-1740/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.meatsci.2013.03.024 F. Yavuz et al. / Meat Science 95 (2013) 798–804 799 high economic growth continues, national consumption and thus Klein, 1996; Hallam & Zanoli, 1992; Perron, 1989; and among others). meat production are expected to increase, resulting in differentiated Time series analysis does not restrict behavior of price and output regional impacts depending upon the strength of the link between in short run, but it reveals the co-movement of variables in the long the farmers and the market. Forecasting these impacts will help policy run. That each of the dependent and independent variables (or a sub- makers drive more accurate conclusions and policy implications for set of explanatory covariates) in itself could be non-stationary, while better decision making for the sector in question. the model, however, assumes stationary of linear combination of the Several Autoregressive Integrated Moving Average (ARIMA) model aforementioned variables. The long run reciprocal relationship be- in this study is estimated using data from the TurkStat and FAO. tween actual output (bovine or mutton meat yield) and its indepen- Section 2 briefly describes the material and methods and is followed dent variables can be shown as by the Results and discussions section. Conclusions and implications for future research are then presented. XJ XM XK ¼ α þ α þ ϕ þ θ þ ; ð Þ Q t 0 jPt; j mZt;m kDt;k ut 2 2. Material and methods j¼1 m¼1 k¼1 Red meat production statistics published by the Turkish Statistical where Q t is a dependent variable (in our case bovine or mutton meat Institute until 2009 did not cover all but only production by registered yield kg/head in the country between 1987 and 2012) observed in slaughter houses and plus slaughtered animals in Feast of the Sacrifice time t, Pt,j is a set of relevant price vector observed in time t for the as 10% of production by registered slaughter houses (TSI). Thus, in this meat product j, Zt,m are other covariates affecting the dependent fl study we used the production estimated by the model proposed by the variable, Q t, while Dt,k are sets of dummy variables that re ect either studies of Yavuz, (2000), Yavuz et al. (2004), Yavuz, Keskin, and Aksoy structural changes or some key policy intervention that the govern- (2006) as follows. This model is also considered as an approach by TSI ment has implemented over time in agriculture such as hybridization after 2009 calculations. support for improving animal breeding, or feed support for improving animal carcass yield across the nation. We use a single price variable M ¼ ½Ã½þS à ðÞÃ1 þ T ðÞ1−D ½ðÞÃB þ Imp–Exp ðÞ1−L −E C in the model reflecting marketing margin, a discrepancy between the retail meat prices (bovine or mutton) and their corresponding whole- Where: sale prices. We assume that as the discrepancy among the marketing margin levels off, the meat yield amount fosters. Also, real national M Red meat production income per capita measured as the US dollars and arable fodder S Number of milking animals areas in hectare are used as additional covariates (Zt,m) to the long T Proportion of animals that give birth to twins run model. The long run reciprocal relation between variables exits D Proportion of new animals that are born dead if there is a co-integration between the dependent and independent B Beginning Inventory of animals variables, resulting in an error correction model (ECM) (Hill et al., Imp Number of animals imported 2011; Wooldridge, 2009). In this context, the short run relationship Exp Number of animals exported L Proportion of mature animals that die XJ XM XK ΔQ ¼ γ þ γ ΔP þ δ ΔZ þ ζ D −λu þ ξ ; E Ending inventory of animals t 0 j t;j m t;m k t;k t−1 t ð Þ ¼ ¼ ¼ 3 C Average carcass weight j 1 m 1 k 1 ¼ −α^ ′ where ut−1 Q t−1 Xt−1 We obtain time series data for bovine1 and mutton primarily from α^ ′ the Turkish Statistical Institute (TurkStat) and FAO.