CHAPTER 4
A SIMULATION MODEL OF TURKEY'S
LIVESTOCK SECTOR

In this chapter, a multi-product, partial equilibrium simulation model is developed to measure the impact of policy changes that are directly and indirectly related to the livestock sector, such as a reduction in grain prices. This chapter describes the basic structure of the supply and demand equations that constitute the livestock sector model. The model description is broken down into four sections: livestock product supply, livestock product demand, price determination and market closure, and feed demand.

4.1. Livestock Supply

Trend growth rates in livestock production provide an indication of the sector's performance in the recent past and likely performance in the near future. Trend growth rates are also a good diagnostic check for estimates of the responsiveness of the sector to income and price changes. Table 4.1 shows that during the 1979-1998 period broiler and egg production has grown at higher rate than other livestock products. The beef and milk production have grown at a rate that nearly equals the growth in population. Mutton (sheep meat) production has grown slower than the population; thus, per capita mutton consumption has declined since the early 1980s.

Table 4.1. Livestock Supply Trend Growth (1979-1998)

The supply component of the model is based on annual data and covers the production of broiler meat, eggs, beef, sheep meat and milk. Production equations for these products were estimated using a single-equation approach based on the assumption that producers are profit maximizers. The general form for the supply of the products is given in equation [4.1].

[4.1] .

Output prices are denoted by p and output by y. The vector w contains prices of inputs, and à is a vector of lagged variables, a time trend, and shift variables. The contents of the w and à vectors vary by commodity because production of different products depend upon the biological constraints of the animals involved.

4.1.1. Broiler Supply

In accordance with an indirect profit function, broiler supply was specified in equation [4.2] as a function of the quasi gross profit margin (QGPM)¹. The QGPM enables us to incorporate several policy variables for policy simulation, and it is more convenient than using separate output and input prices.

[4.2]

4.1.2. Egg Supply

Turkey's egg supply is specified in equation [4.3] as a function of the QGPM, the lag of the QGPM, a time trend (T), and a dummy variable.

[4.3] .

4.1.3. Beef Supply

The supply equation for beef and veal is specified in equation [4.4] as a function of the lag of the beef producer price, the QGPM for cow milk, and a time trend. The QGPM for cow milk was incorporated into the beef supply equation to capture the impact of dairy production on beef output. The time trend (t) was added to the à vector for the beef equation to capture the effects of technological change on beef output.

[4.4] .

4.1.4. Sheep Meat Supply

Given the biological relationships in sheep meat production, the supply equation for sheep meat is specified in equation [4.5] as a function of the lag of sheep meat production and the barley price as an input cost². The barley producer price was used in the right hand side of the supply equation because barley is a primary feed source in the sheep sector in Turkey.

[4.5].

4.1.5. Milk Supply

Milk supply exhibits simultaneity in determination with the milk price, so we first estimated the milk price is as a function of beef price and feed price. Then the supply equation for milk was specified as a function of the estimated milk price and a dummy variable as a shift variable. The milk supply function is shown in equation [4.6].

[4.6] .

Table 4.2 summarizes the elasticities used in the supply equations discussed above. Elasticities from other studies of Turkey's livestock sector are included for comparison.

Table 4.2. Supply Elasticity Estimates for Livestock Production in Turkey

*Source: John C. Beghin (1997).

4.2. Demand for Livestock Products

In this study, livestock product demand is derived from single-equation demand function, because it is a stable construction that works well in simulation models.

Moreover, the single equation structure captures the essential own-price and income effects that are the primary drivers in food demand. Cross-price effects are also included in most demand equations to capture the substitution effects caused by changes in prices of competing livestock products. Elasticities used in the demand equations are shown in Table 4.3 along with comparison elasticities from other studies of Turkish livestock product demand.

4.2. 1. Demand for Broiler Meat

Per capita chicken consumption is modeled in equation [4.7] as a function of the chicken price normalized by the consumer price index (CPI), per capita real GDP (Y), and a lag of the per capita consumption. The lagged dependent variable reflects habit persistence of consumers. We tried to include prices of substitute meats and get cross-price relations in the demand model.

[4.7] .

However, the parameters obtained from the model were insignificant and signs of the parameters were not as expected for the prices of substitute meats. Since the purpose of this study is to measure the impact of the alternative policies on the livestock market, it is more appropriate to include prices of the substitute products (beef and sheep meat) as explanatory variables. To fulfill this objective, following synthetic demand equation was used in the simulation model instead of the equation (4.7). The elasticities used in the equation (4.8) were obtained from Koç (1999).

[4.8]

4.2. 2. Demand for Eggs

The egg demand model is defined in equation [4.9] as a function of the egg price normalized by the CPI and per capita GDP³.

[4.9] .

4.2. 3. Demand for Beef and Veal

Per capita beef and veal consumption is modeled in equation [4.10] as a function of beef and chicken prices normalized by the sheep meat price, per capita real GDP (Y), and time trend reflecting changes in taste and preferences of consumers.

[4.10] .

4.2. 4. Demand for Sheep Meat and Milk

The sheep meat and milk demands are calculated using the synthetic demand equation [4.11]. Demand is a function of per capita real GDP, the good's own price (P) and substitute prices (P^s). The coefficients are the income, own-price, and cross-price elasticities, respectively, that were borrowed from previous demand studies.

[4.11]

The elasticities reported in Table 4.3 for this study show that livestock products are necessities in consumption, except broiler meat. Both the supply and demand for broilers are elastic. In other words, a change in real or relative prices will have a substantial impact on production and consumption. Moreover, there are substantial complementary and substitution effects between livestock products. For example, a 10 percent decline in the consumer price of broiler, ceteris paribus, will reduce beef demand about 7.5 percent.

Table 4.3. Income and Price Elasticity Estimates for Livestock Product Demand in Turkey

Source: John C. Beghin (1997), * Kasnakoðlu (1995)
* Note: Koç (1999) and Koç and Tan (2000) estimated the elasticities for sheep meat, broiler and milk respectively.

4.2. 5. Data, Estimation, and Parameters

Production, disappearance, and trade data were obtained from Ministry of Agriculture and Rural Affairs. The prices of feed, meat, milk, and eggs were obtained from the State Institute of Statistics. International historical prices and projections, EU policy prices, and EU policy variables come from the Food and Agricultural Policy Research Institute (FAPRI) at Iowa State University. Most of the behavioral equations were estimated by ordinary least squares (OLS) in double-log, lin-log or linear forms, except the synthetic demand equations for broiler meat, milk, and sheep meat. Preliminary tests indicated that simultaneity was a problem in the broiler meat, milk and sheep meat supply equations and the egg demand equation. This problem required the use of two-stage least squares (2SLS) or an instrument variable (IVM) estimator to obtain unbiased parameters estimates. The parameter estimates and diagnostic statistics of the models are presented in the Appendix.

4.3. Price Determination and Market Closure

Up to this point, we have described the domestic supply and demand components of the simulation model, but closure of the model requires a specification of stocks, net trade, and prices. The structure of the model equilibrium is different for each type of livestock product in the study due to data availability for stocks, trade, and representative international prices. Consequently, the domestic price determination process also varies according to data availability.

Essentially, two approaches are taken to achieve equilibrium for total supply and demand for each commodity. First, in the broiler, egg, and milk components, the stocks and net trade (except milk) are assumed to remain constant in the projection period at the average value in recent years. In the case of milk, a net trade equation is specified as a function of lagged imports and international cheese prices. With stocks and trade determined, these markets reach an equilibrium by solving for the domestic price that equates total supply and demand.

The second approach is used in the beef and sheep meat sectors. In these components, the domestic market price is determined as a function of international prices (beef) or the price of the major competing product and domestic supply (sheep meat). Specifically, the beef producer price in Turkey is derived using a price transmission equation that depends on the world price for beef. The domestic producer price of sheep meat is determined by the quantity of sheep meat supplied and the beef price. Stocks are assumed to remain constant at recent levels; therefore, net trade is computed as the excess supply or demand existing in the market.

4.4. Feed Demand

As pointed out in Chapter 1, the demand for feed stuffs in Turkey has risen as livestock output has grown. Moreover, the use of quality feeds is critical to the continued growth in productivity in Turkey's livestock sector. In this section we describe a model for feed demand that can be used to assess Turkey's feed requirements in the future.

The demand for feed is derived from the production of livestock products. Equation [4.12] illustrates the relationship between feed demand and livestock production, namely, that the total demand for feed i (d_i) is the sum over all livestock products of the per-unit input of feed i (c_ij) into livestock product j times the output of livestock product j (X_j).

[4.12] .

It is evident from equation [4.12] that changes in either the level of livestock production or the per-unit feed requirement will cause total feed demand to change. The livestock supply model generates output levels for livestock products, and changes in these levels are used in calculating feed demand changes. The unit-input coefficients can either be viewed as fixed over time or as changing in response to prices for livestock products and feed grains.

When the coefficients are allowed to change, the rate at which they move and the direction are determined by parameters imbedded in the livestock production technology. The livestock industry in Turkey, as in many middle-income countries, embodies a broad mixture of production technologies, ranging from small farms utilizing traditional techniques to large, capital-intensive commercial operations. The total demand for any given feed used in the production of a livestock product will be the sum of demands created by each production technology. For example, feed conversion ratios and feed inputs for beef producers raising domestic cattle breeds differ from those beef producers raising cultured or crossbred cattle.

The total demand for corn used to produce beef is calculated as the sum of corn fed to domestic cattle, cultured cattle, and crossbred cattle. As the technological mix of production changes in Turkey, the level and composition of total feed grain demand will also be altered. In order to capture these effects, feed demands are generated by equation [4.13]. Changes in technology and industry composition are effected through the feed conversion ratio (FCR), livestock yield, and technology share (domestic, culture, and crossbred). These coefficients are treated as parameters and change according to exogenously determined time paths.

[4.13]

The relative prices of various feed stuffs will also affect the demand for particular feeds. Following Fuller et al. (1999), the authors estimated a translog feed demand system for ruminant meat and dairy production using the AERI feed survey data. Feeds were aggregated into 4 categories for beef and dairy cattle: formula feed, grain, forages, and by-products and meals. The feeds contained in each aggregate correspond to Table 3.6. In the fed sheep and dairy sheep sectors, fewer producers feed unprocessed grain than cattle producers, so the grain and by-product and meal categories were combined. The estimates produced the matrix of demand elasticities shown in Table 4.4.

The beef sector estimates did not provide satisfactory results for the formula feed and forage equations. Thus, the coefficients for the own-price and formula feed-forage cross price term were adjusted to eliminate complementary effects and to ensure downward-sloping demand curves. From equation [4.13], livestock production, the FCR, and the technology share determine a total quantity of feed consumed by each animal type. The price elasticities in Table 4.4 were used to adjust the share of each feed in the total ration based on changes in the price indices of the major feed aggregates. All feed types within an aggregate have the same response to changes in the feed price indices. The price indices are constructed as a weighted average of the component feed prices using the average quantity shares from the survey data to weight prices. These shares are held constant in the projection period. Prices for forages were not available, so weighted average of gross revenues for competing crops was used as a proxy for the forage price index.

Table 4.4. Feed Demand Elasticities

Limitations are placed on the growth of demand for particular byproduct feeds and forages. For example, bran and sugar beet meal feed use is capped in proportion to the production of wheat and sugar in the projection period. Likewise, silage and straw feed use are limited in proportion to corn harvested area and wheat and barley area, respectively. When the feed use constraints are binding, excess feed demand is converted into an equivalent quantity of corn, wheat, barley, or soybean meal on an energy or protein feed value basis. Given the above description of the major model components, the interaction between the livestock supply model, feed demand model, and exogenous variables is summarized in Figure 4.1. The exogenous crop variables (crop area, crop production, and yields) are computed in the Turkish Agricultural Policy Analysis Model (TAPAM) (Koç et al., 1998)⁴ and are used in the feed demand model to compute price indices and to restrict bran and silage demand. International prices from the FAPRI outlook are used to derive domestic crop beef prices. The macroeconomic projections for population, income growth, exchange rates, and price deflators are used in the livestock supply and demand equations described above. The livestock model outputs livestock production levels that are used to compute the associated feed demands. Finally, livestock product trade and total feed demands are the final outputs from the livestock and feed demand models.

In this chapter, the core livestock supply, livestock demand, and feed demand components of the simulation model were described. In the next chapter, this simulation model is used to project Turkey's livestock supply and utilization and feed demands out to the year 2009. The specification and estimation results presented in this chapter do not constitute the entire set of equations in the model. Specification and estimates of auxiliary equations are not presented here, but they may be obtained from the authors upon request.

1 The QGPM is calculated as a producer's output price less the feed price, because feed comprises about 70 percent of production cost of broiler and egg (Yurdakul et al., 1999). Since simultaneity exists in the broiler market, Instrumental Variable Method (IVM) was used to estimate broiler supply using the beef producer price and a time trend as instruments.
2 It was verified that the simultaneity exists in the sheep meat market. To clear the market, the price of the sheep meat was estimated as a function of sheep meat production and beef price.
3 Since simultaneity exists in the egg market, 2SLS was used to estimate model. In the first stage, the egg price is determined using current year egg supply and broiler price as an explanatory variables.
4 It is up dated and up grated by AERI (2000).