Overview of the Turkish sheep sector and support policies

The production of animals is important in terms of providing the nutritional requirements of a population. The amount of livestock production should be increased in accordance with the increase in population. Despite slowing down, the population growth rate in Turkey continues. However, the production of red meat in particular has not increased in parallel with the increase in population. When viewed from this perspective, as with the production of all animals, the production of sheep, which is important in terms of meat and milk production, must be increased.

When the number of animals is considered, it can be said that Turkey is significantly engaged in animal husbandry. According to the FAO (2019), while Turkey is ranked eighth in the world in terms of the number of sheep, it is ranked second after the United Kingdom when compared with EU member countries. Even though Turkey has a huge potential in terms of sheep breeding, over time it has partially lost this potential. Despite the fact that Turkey’s ecological conditions are suitable for small ruminant rearing, the current situation of the sector is still far from meeting expectations. The sheep population in Turkey declined by 7.8% in the last two decades reaching 37.3 million heads, 91.7% of which consists of a domestic race. In the same period, the number of slaughtered sheep decreased by 36.2%, while sheep meat production decreased by 15% (Fig. 1a). However, the share of the Merino race increased fourfold in the last two decades, which caused an increase in wool production by 16.7%. In addition, sheep milk production increased from 1.1 million tons to 1.5 million tons in the same period. The average meat yield was 21.6 kg per slaughtered sheep in 2019. Considering the foreign trade of the sheep sector in Turkey, no live sheep exportation was carried out. On the other hand, the live sheep import value of Turkey was 37.3 million dollars in 2017. Live sheep import has changed in accordance with the domestic demand over the years (Fig. 1b). Sheep meat trade is also a negligible level. According to TURKSTAT (2020) data, in the last decade, the increase in the real consumer prices for sheep meat (56%) was higher than the increase in real producer prices (24%).

In the last century, the Turkish government has implemented a number of policies aimed at improving animal production. However, the outcomes of these policies have been dissatisfactory. While the support for plant production is substantial within the agricultural policies in Turkey, the support for animal production has remained at a more limited level. The limited livestock support was concentrated primarily on increasing poultry and then dairy cattle farming, and neglected ovine breeding. During this period, support in other areas such as meat incentive premium, medicine and feed premium support regarding sheep and goat farming were provided for a very short time. As a result, significant problems have emerged in the livestock sector, particularly in the small ruminant sector. These policies implemented during this period have adversely affected both the quality and quantity of small ruminant production. This has also caused instability in product prices and the incomes of producers. Consequently, sheep breeding has lost its appeal in Turkey and as of the mid-1980s, sheep stocks have reduced by 36%. Overall, this has adversely affected the competitiveness of the sector.

In the year 2000, the Turkish government started a new agricultural support system that was based on both internal and external factors in Turkey and changed the aims and instruments of agricultural support policies. Since then, several new regulations related to livestock support have been issued and the share of livestock supports in the total has increased from 0.5% to 30% (Anonymous, 2018). In the context of these improvements, sheep breeding farmers were provided 25 Turkish Lira (TL) per breeding female sheep in 2018. Additionally, vaccination subsidy for brucellosis and foot and mouth disease and tagging subsidy for small ruminants were provided to farmers separately as 1 TL per head. In the same year, shepherd support of 5000 TL was provided to farms that had a flock of at l200 or more sheep. The condition for the number of sheep was reduced to 100 in 2019. In addition, forage crop support based on the area was also provided to sheep breeding farmers. Despite these supports, the small ruminant sector in Turkey has not yet developed at the targeted level. The structure of sheep breeding in Turkey consists of low-yield native breeds that depend mainly on extensive grazing. Various problems are encountered in this sector such as the lack of input supply including feed and shepherds, the lack of product marketing and assessment opportunities and small- scale subsistence farming.

Figure 1. Developments in numbers of sheep and sheep meat production (a), live sheep trade value (million $) (b) in Turkey. Source: TURKSTAT (2020)

Data and modelling assumptions

The required primary data were obtained through an inclusive questionnaire applied to 110 farmers from the provinces of Niğde and Aksaray in Turkey in 2018. According to TURKSTAT (2019) data, there were approximately1.1 million sheep in the TR71 region. According to the Classification of Statistical Regions (SRE) at level 2, the TR71 region is comprised of the provinces of Kırıkkale, Aksaray, Niğde, Nevşehir and, Kırşehir. The provinces of Niğde and Aksaray constituted 70% of the total sheep amount in the region and 3.2% of the country. These two provinces were selected as the study area as sheep breeding is intensively carried out in both provinces. The Stratified Random Sampling method of Yamane (1967) was used to determine the sample size.

All variables of the model were calculated as an Animal Unit Equivalent (AUE) in order to adjust the different kinds and classes of livestock in a common form. The AUE coefficients used in the study were established according to the Pasture Regulation no 1998/23419 (Anonymous, 1998). The inputs in the TE analysis consisted of five components: total feed use (kg/AU), vaccination and veterinary expenses (TL/AU), total labour force use (man hours/AU), animal purchasing (TL/AU) and other variable costs (TL/AU).

The live carcass weight of sheep was selected as the output variable. All of the feeds used were converted to dry matter feed equivalent. The labour variable was comprised of both the hired and family labour used in sheep rearing throughout the year and was expressed as man-hours per AU. The animal purchasing costs covered the replacement of the herd. The other variable costs covered the relevant running costs such as electricity, water, disinfection, transportation, maintenance, equipment repairs, and pasture rent. The factors influencing inefficient sheep breeding were the region of the production unit, grazing duration, farmer experience, sheep race, subsidies, herd size, level of family labour used and access to extension services. Grazing duration was expressed as the days spent on the pasture during one year. Farmer experience was calculated as number of years. For each farm, the share of sheep breeding subsidies received in total gross income was taken as the subsidy rate. The inefficiency model consisted of four dummy variables related to sheep race, region and access to extension services. In the dummy variable related to sheep race, farmers were given a value of 1 if they reared Akkaraman race sheep and 0 if they reared other races. The dummy variable related to region was also included to reflect the regional variations between the provinces. In the study area, 45% of the sample farms were located in Aksaray, while the remaining 55% were located in Niğde.

The most difficult task when creating a PAM table, is estimating the social prices for inputs and outputs. Many factors such as subsidies, import tariffs, indirect taxes, overvalued exchange rates, overstated labour costs, other forms of administrative interventions and monopoly prices affect the domestic market prices of many products and services (JICA, 2015). In order to calculate the social prices in the PAM analysis, secondary data such as production subsidies, exchange rates, world prices, and import or export tariffs were also used. For this purpose, the inputs were disaggregated into tradable and non-tradable components, which is also a challenging task. Depending on whether the commodity was an exported or imported substitute, the CIF (cost insurance and freight) and FOB (free on board) prices were taken from TURKSTAT (2018) to calculate the social (reference) prices for tradable feeds and live animals. These prices were converted into local currencies by reel exchange rates. The social farm gate prices for both imported inputs and outputs were derived from the CIF price in domestic currency by adding on storage and transportation costs. To calculate the social farm gate prices for the exports, the storage and transportation costs were subtracted from the FOB prices calculated in domestic currency (Gürer et al., 2017).

As the border prices of commodities such as agricultural labour, interest on working capital and other running costs were not available; these variables were considered as non-tradable goods and services.

Several methods were used to calculate the social values of these variables. To calculate the social value of the working capital, the reel interest rate was calculated in accordance with the methods of Mane-Kapaj et al. (2010) and Gürer et al. (2017). To convert the labour wage into an economic one, the Labour Conversion Factor (LCF), also known as the Shadow Wage Rate (SWR) was determined. The LCF is specified as the shadow wage (price) ratio (SPi) to a domestic market wage (price) (MWi) and calculated according to Eq. 1 (Jayanthakumaran, 2003).

In this study, the shadow wage was considered as a marginal productivity of labour and estimated as a ratio of the value of total agricultural output at market prices to the total agricultural labour force at a national level (JICA, 2015). The literature review showed that many studies had used the conversion factor based on domestic price to estimate the reference prices of labour (Chen, 1993; Gürer et al., 2017). Hence, the conversation factors of labour for the farms in the provinces of Niğde and Aksaray and the overall farms were calculated as 1.16, 1.60 and 1.41, respectively.

In addition, to reflect the effect of the distortions of agricultural policies, all input subsidies including vaccination support per animal, shepherd employment support and forage crops subsidy were subtracted from the cost calculated at market prices. Similarly, the payment of animal breeding per head was added to the annual gross income of sheep breeding calculated by adding the incomes acquired from animal, milk, manure and wool sales and stock residual value at market prices.

Stochastic frontier analysis

In this study, the SFA was used to estimate TE and the determinants of the inefficiency in sheep production depending on stochastic and unpredictable natural/environmental conditions. As the SFA method takes into account the random noise as part of the deviation from the production frontier, it was determined as the most appropriate method for this study. Frontier models have been commonly used by researchers to examine the TE of farms or farm activities (Battese, 1992; Bravo-Ureta et al., 2007; Latruffe et al., 2016).

In this study, the stochastic frontier function was represented by a log linear Cobb-Douglas production function, which was derived for the sheep-rearing farm to determine a possible increase in output without changing the uses of the inputs. By following the model of Battese & Coelli (1995), the production function and exogenous effects affecting the inefficiency were simultaneously estimated. Technical efficiency was estimated relatively according to the best performing farms in the sample. The equation of the SFA model is specified as follows:

The SFA explains that the output quantity of ith farm determines both the efficiency of a subject and the noise effect (vi). The noise effect, which represents the effect of random external factors, can be positive or negative. In addition, ui is a non-negative random variable related to inefficiency. Hence, the SFA model consists of a deterministic frontier, a noise effect and inefficiency.

To estimate all parameters of the maximum likelihood function, FRONTIER 4.1 software was used. The software estimates the γ = σ2 /σs2 parameter, which takes a value between 0 and 1. The zero value of γ shows that all the deviations of the frontier could be explained completely by noise, while a value of 1 indicates that the deviations could be explained by technical inefficiency.

The TE of ith farm is provided by the ratio of the observed output of the ith farm relative to the potential output estimated by Equation 2. Thereby, technical efficiency (TEi) is found using Equation 3:

The TE coefficient acquires a value between 0 and 1, indicating farm efficiency between 0% and 100%.

Determining the presence of systematic inefficiency is important in the SFA. For this purpose, the inefficiency factors (TE effects) model, which includes external factors, was applied to the model in a single stage. The TE effect model (Equation 4) was obtained by including a linear function of the external factors of “u” in Equation 2 into the model. In Equation 4, ‘z’ is the explanatory external factors vector and ‘δ’ is the variable coefficient in the vector (Battese & Coelli, 1995).

Hypothesis tests for SFA

Four main hypotheses were tested in the study by using the generalized likelihood ratio (LLR) tests. The results of these hypothesis tests are presented in Table 1. The first was related to the functional form. The null hypothesis was accepted concluding that the Cobb-Douglas production function was a more adequate representation model in the stochastic frontier. The second hypothesis was conducted to test the effects of technical inefficiency. This was rejected, as the estimated generalized LLR test statistic of 77.50 was higher than the critical value range of 21.67 at a 1% significant level. The third hypothesis was that the model was an average response model, which implies that all the inefficiencies were due to factors outside the control of the farmers. This was rejected as the estimated generalized LLR test statistic was significantly different from zero at 1%. This implies that the ordinary average response function is not a suitable specification for sheep breeding in the area. Thus, non-controlled factors by the farmers were also responsible for the inefficiencies. The fourth test was conducted with the null hypothesis that eight specific explanatory variables did not have an effect upon the technical inefficiency. This hypothesis was also rejected indicating that the joint effect of these eight factors on the levels of technical inefficiency was significant, even though the individual effects of some of the variables were not statistically significant.

 

Table 1.Hypothesis tests for SFA

 

Policy analysis matrix

This study used PAM to investigate the competitiveness of sheep breeding. PAM, which was developed by Monke & Pearson (1989) and developed by Masters & Winter-Nelson (1995), measures the efficiency of input use in production, comparative advantages between commodities and the level of government interventions. PAM is a quantitative framework based on benefit-cost analysis and is used to evaluate the influence of policies on commodities. Costs and revenues are calculated for both market prices, the current observed prices paid or received by farmers and reference prices, the effect of distorting government policies such as subsidies and taxes on market prices (Table 2).

According to Table 2, a PAM table has two accounting identities. The first one comprises the profitability by calculating the difference between revenue and cost, while the second one measures the effects of distorting policies and market failures by calculating the difference between the observed market prices and prices without market distortions (Monke & Pearson, 1989). Hence, a PAM can account for the existing economic efficiencies of a commodity production system, the distortion degree on input and output markets, and the extent of resource transfers within an economy. Private profit (D) is the difference between revenues (A) and costs (B+C) in observed prices. Social profit (H), like private analogue, is the difference between revenues (E) and costs (F+G) in reference prices. Output transfers (I) and input transfers (J) arise from two kinds of policies that cause divergences between observed and world product prices: commodity-specific policies and exchange-rate policy. Factor transfer (K) causes private factor costs (C) to differ from social factor costs (G) and can be either positive (causing an implicit tax or transfer of resources away from the system) or negative (causing an implicit subsidy or transfer of resources in favour of the agricultural system). The net transfer caused by policy and market failures (L in the matrix) is the sum of the separate effects from the product and factor markets, L = (I - J - K). The net transfer can also be found by a comparison of private and social profits (D-H).

Through PAM, it is possible to calculate a set of indicators assessing the profitability and competitiveness of a commodity system. The nominal protection coefficient for outputs (NPCO), the nominal protection coef ficient for inputs (NPCI) and the effective protection coefficient (EPC) are indicators that highlight the source and degree of the protection of a commodity system, providing information on the incentives or disincentives in the sector.

A PAM table can also be used to calculate the relative efficiency or comparative advantage coefficients of commodities. The first of such coefficients is the domestic resource cost (DRC), which has been widely used in the literature for this purpose (Fang & Beghin, 2000; Mohanty et al., 2003). The DRC coefficient measures the opportunity cost of domestic resources used in production to add value, both assessed in reference prices. The social cost benefit (SCB) coefficient compares total costs to total revenue which are evaluated at reference prices. Another indicator of measuring competitiveness is the private cost ratio (PCR), which compares the efficiency of the domestic factors in market prices under current policies.

 

Table 2.Framework of policy analysis matrix (PAM)

AU: animal unit. SD: standard deviation. TL: Turkish lira. Source: Author’s calculation (2018)

 

Figure 2.  Distributions of technical efficiency scores. Source: Author’s calculations

 

 

Competitiveness and sustainability in sheep breeding

The main results of the PAM analysis for sheep breeding in the two provinces and overall farms are presented in Table 5. The PAM results indicated that the policy had an effect per AU. The private profits of sheep breeding were calculated as 1368.67 TL/AU for the overall farms, 1052.61 TL/AU for the farms in Niğde and 1684.02 TL/AU for the farms in Aksaray. However, the negative social profits in the study area revealed that sheep breeding in this area depended on government support. Hence, the farms were not economically profitable or were economically inefficient. The results showed that sheep breeding was privately profitable in the region under the current policies. In general, the results of the PAM analysis showed the effects of the divergences and the efficient policy. Accordingly, due to the supports provided the sheep breeders in the study area benefited from the internal resources at a cheap price. Expenses on the domestic production factors were 2321.86 TL/AU in market prices, which was lower than the level of social prices (2724.32 TL/AU). The same situation was observed with the tradable production factors. The tradable input costs paid by the sheep breeders in market prices were 2293.28 TL/AU, and in social prices were 3607.09 TL/AU. Moreover, the agricultural supports caused an increase in revenues from 5483.37 TL/AU to 5983.80 TL/AU. As a result, there was a transfer of 2216.71 TL/AU from the government to the farmers.

The summary results of the protection coefficients and incentives for PAM are presented in Table 6. Government policies can influence producers, consumers and traders in a commodity system. This influence can be both positive and negative. A PAM analysis can be used to show how government policies influence a commodity system. Nominal protection coefficients (NPCs) show the extent of the divergence of domestic prices from world (social) prices. Such divergences may be a result of the impacts of policies such as trade policies, market imperfections and state agricultural policies. The result of NPCO was equal to 1.09 for both provinces. As its prices were above social prices, sheep breeding was protected by means of support policies. Therefore, the producers received prices that were 9% higher than the border prices. In terms of NPCI, which shows the degree of transfer or the level of protection of tradable inputs, the result was equivalent to 0.57 in the farms in Niğde, 0.69 in the farms in Aksaray and 0.64 for the overall farms.

The EPC ratio shows the degree of the incentives offered to producers from output and tradable input distortions. Sheep farmers in Niğde and Aksaray and the overall farms received much more support for their value-added at the level of 115%, 85% and 97%, respectively.

The competitive indicators, namely PCR, DCR and SCB, are reported in Table 6. PCR indicates the percentage of the value added used to remunerate the factor of production, i.e. the components of the value added. The PCR for sheep breeding was equal to 0.63 overall, meaning that only 63% of the value added was absorbed by the remuneration of the production factors. The values of DRC in both provinces were >1, meaning that domestic costs were in excess of social costs. Therefore, excluding the efficient farms, the sheep farms in both provinces had no comparative advantage. Furthermore, the inefficient use of the country’s resources for sheep breeding was higher in the farms in Niğde than in the farms in Aksaray.

Similarly, the SCB value, another indicator of competitiveness, was found to be >1 in both provinces. This indicates that the total input costs were greater than the revenue and sheep breeding was not competitive. However, the SCB value for the efficient farms was found to be 0.80, which shows that these farms were competitive.

In this study, the association between sheep breeding competitiveness and TE was analysed using Kendall’s tau_b rank correlation analysis. The correlation coefficients between the TE scores and SCB and PCR were found to be -0.56 and -0.50, respectively, indicating that there was a statistically significant negative correlation between them at a 1% significant level. This finding implied that many farms with relatively high TE scores In this study, the association between sheep breeding competitiveness and TE was analysed using Kendall’s tau_b rank correlation analysis. The correlation coefficients between the TE scores and SCB and PCR were found to be -0.56 and -0.50, respectively, indicating that there was a statistically significant negative correlation between them at a 1% significant level. This finding implied that many farms with relatively high TE scores.

 

Table 4.Model results of stochastic frontier and inefficiency.

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