The Feasibility of Milk Protein Production in the United States

A Report to Dairy Management Inc.

Tuesday, May 6, 2008

byCharles Nicholson, Ph.D. and Mark Stephenson, Ph.D.

The Feasibility of Milk Protein Production in the United Statesby

Charles Nicholson, Ph.D. and Mark Stephenson, Ph.D.

Executive Summary

Prior to the 1950s, the United States produced a significant amount of casein. Since that time, casein has been imported from lower cost countries and the primary dairy protein ingredient produced domestically has been nonfat dry milk powder. More recently, other dairy proteins have been imported into the United States in the form of milk protein concentrates. In the past 5–7 years, many economic variables, such as dairy policy and exchange rates, and have changed. And, significant growth in global demand for dairy products has occurred at a time when climatic impacts have reduced global milk supplies. This report attempts to answer the question as to whether it is now profitable to produce milk protein products (casein, caseinates and milk protein concentrates) in the United States.

A dynamic model of the United States and the rest of the worldʼs dairy industries was developed. The model includes 17 final and intermediate dairy products and is highly detailed in inventory behavior of storable dairy products, milk pricing regulation, domestic dairy policies, and farm milk supply response. Manufacturers respond to profitability by increasing or decreasing plant capacity over time.

Static profit estimates from the model suggest that:• Nonfat dry milk production has been a break-even proposition over the last few

years.• Casein, caseinate and low-concentration MPCs would have been profitable to

produce in 2006, 2007 and in early 2008.• Midrange and high concentration MPCs would have only been profitable to

produce in early 2008.

Dynamic modeling further suggests that:

• Casein, caseinate and low-concentration MPCs have a high likelihood of being profitable even if milk production in Oceania rebounds.

• Dairy farmer net revenues are increased over the five-year period after production of protein products comes on line, based on simulations using 2006 as a base year.  However, initial increases in milk prices are likely to generate a milk supply response that will erode initial price gains over time.

• Using dairy producer check-off dollars to promote protein product manufacture through production subsidies or risk reduction programs has a benefit-cost ratio much greater than one for the programs evaluated.  Investments in these

• programs return nearly four dollars to dairy farmers for every dollar expended.  However, the programs that are the most beneficial have costs in excess of $50 million over five years.

• The size of the US market is an important consideration for companies considering investments in dairy protein product manufacturing capacity.  The number of large-size plants needed to supply the US market is small (ranging from 1 to 9 for the five products examined).  This suggests that initial entrants into protein product manufacture will benefit more, but also that over-investment in the market may easily erode the profitability of the product if only domestic markets are targeted.

The Feasibility of Milk Protein Production in the United States

Charles Nicholson, Ph.D. and Mark Stephenson, Ph.D.1Tuesday, May 6, 2008

Introduction

Milk is a complex assembly of water, fat, proteins, lactose and minerals. For more than a century, these principle milk components have been separated into cream (fat rich) and skim milk (protein, lactose and mineral rich) fractions and used to manufacture a variety of intermediate and final consumer products. For many years casein, one of the milk proteins, has been harvested as an intermediate product and more recently, milk protein concentrates (MPCs), concentrated casein and whey proteins, have been used in a variety of consumer dairy, food and industrial products.

The United States produced casein for many decades prior to the 1950s. However, reported casein production in the U.S. was dropped as a series in 1957 because of very small production volumes. The decline in U.S. casein production was largely due to less expensive imports of the product. Imports of milk protein concentrates into the U.S. also began to increase in the late 1990s. Many economic variables have changed since 2000 and this report attempts to answer the question as to whether it is now profitable to produce milk protein products (casein, caseinates and milk protein concentrates) once again in the United States.

Products Considered

We looked at five different protein products: a lower, mid and high range MPC product, acid casein, and sodium caseinate. The lower MPC product was modeled as a 42 percent protein powder, the mid MPC at 70 percent and the high concentrate as a 90 percent MPC. These products were chosen to reflect product categories reported in a 2004 study by the U.S. International Trade Commission (USITC) on the competition for milk protein markets in the U.S.2 MPCs with a protein content from 40 to 90 percent are categorized under Chapter four of the Harmonized Tariff Schedule (HTS 0404.90.10) and MPCs with a protein content greater than 90 percent under Chapter 35 (HST 3501.10.10). Casein is classified under HTS 3501.10.50 and caseinate under HTS 3501.90.60.

1 The authors are a Senior Research Associate and a Senior Extension Associate at Cornell University. They are both a part of the Cornell Program on Dairy Markets and Policy.

2 US International Trade Commission. 2004. See references.

The USITC study was instigated at a time when imports of protein products—particularly lower concentrate MPCs—had increased at a rapid rate. Figure 1. shows a moving average of protein product imports from 1996 through 2007. The combined low and mid-range MPCs clearly show the increase during the 1998-2000 time period. A 12-month moving average is shown to reduce the degree of monthly import variation.

Figure 1. Casein and MPC Imports 12-Month Moving Average, 1996-2007.

0

1000

2000

3000

4000

5000

6000

7000

Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07

000

MT

Low Mid MPC High MPC Casein Caseinates

Production Processes

The principal constituents of milk are water, fat, proteins (caseins and whey, sometimes called serum or albumins), lactose (milk sugar) and minerals (salts or ash). The proportion of these fractions will differ from cow to cow, by stage of lactation, between farms, from one region or country to another, season to season, etc. The composition of milk in the U.S. tends to be lower in fat and proteins and higher in lactose compared to other major milk producing countries of the world. For the U.S., typical milk compositions would be 87.7% water, 4.9% lactose, 3.4% fat, 3.3% protein, and 0.7% minerals.

After pasteurization, milk received at a plant is typically separated into cream and skim milk fractions. The cream fraction contains most of the butterfat and some of the rest of milkʼs constituents. The skim fraction contains very little butterfat and most of the rest of

– 2 –

the water, protein, lactose and minerals. It is this skim milk fraction that is used to harvest the protein rich products that are considered in this report.3

The simplest dry milk protein product is nonfat dry milk powder. This is made by condensing the skim milk fraction and then drying the condensed milk into powder. As seen in Table 1, the protein content will vary according to the composition of the milk that was used. A slightly more processed product is skim milk powder (SMP) that is protein standardized. Skim milk powder is a world standard for trade and has 34 percent protein. Nonfat dry milk typically will have a higher protein content and can be standardized down to 34 percent by adding lactose to the skim milk before drying.

Table 1. Average Composition of Dairy Products (%).

Product Moisture Fat Protein Lactose Minerals

Nonfat Dry Milk 3.2 - 4.0 0.7 - 0.8 35.1 - 36.2 52.0 - 52.2 7.9 - 8.0

MPC 42 3.5 1.0 42.0 46.0 7.5

MPC 70 4.2 1.4 70.0 16.2 8.2

MPC 90 9.5 0.9 90.0 0.5 1.4

Casein (Acid) 9.0 1.0 90.0 0.1 2.2

Caseinate 3.8 1.1 91.4 0.1 3.6

Milk Protein Concentrate Production

Milk Protein Concentrates have been produced with two different methods: blending dry ingredients and membrane concentration. There isnʼt a strict definition or product identity for MPCs although it is widely understood to be milk proteins (caseins and whey) in the approximate proportion found in cowʼs milk and reduced in lactose content to achieve the desired protein level.

The European Union has typically produced MPCs by blending either skim milk power or whey protein concentrate with casein or caseinate. The vast majority of MPC imports into the U.S. come from Oceania (New Zealand and Australia). These MPC products are produced using a membrane filtration process to concentrate the proteins in skim milk before drying. MPCs produced under the two methods have different functional characteristics and, because the membrane technology appears to be favored for use in the U.S., it is this process that we have modeled.

– 3 –

3 Whey proteins are the major competitor to the protein products examined in this report. Whey proteins are typically harvested as a by-product of the cheese making process.

There are four common membrane technologies that are used in food processing: Reverse Osmosis (RO), Nanofiltration (NF), Ultrafiltration (UF) and Microfiltration (MF). The chief distinction between these processes is the size of the membrane pore. Particles pass through into the permeate or are retained in the retentate. Ultrafiltration is the process used to produce MCPs.

A UF membrane may be made of polymers or ceramics and will have a pore size of 10-2 – 10-1 µm. This pore size will allow water, minerals and lactose to pass through but will retain the fat and proteins of the milk.

A MPC 42 can be processed with a single pass through a membrane system such as the one depicted in the Figure 2 and then concentrated to remove water with an evaporator and finally dried to a powder. A MPC 70 requires an extra step in the membrane process. This extra step is called diafiltration and is simply the addition of water to the retentate of the first step and a second (or third) pass through the UF membrane to increase protein to the desired level by “washing out” additional lactose and minerals.

Figure 2. Production Module for UF Processing.4

The diafiltration process can produce MPCs up to a level of about 85 percent proteins. At or above this level of concentration, the number of diafiltration steps becomes

– 4 –

4 Dairy Processing Handbook. See TetraPak reference.

expensive. Our calculations of theoretical yield indicates that it would be difficult, regardless of the number of diafiltration steps, to achieve a 90 percent MPC. The small amount of residual fat in skim milk becomes limiting at this level. Butterfat as well as protein is concentrated in the permeate and at high levels of MPC you simply may not be able to attain 90 percent protein.

We do think that with excellent quality control on the cream separator that MPC 90 could be accomplished. However there are two other processes that can also make a 90 percent milk protein product: ion exchange to produce milk protein isolates or a co-precipitation process. Although we recognize functional differences in the three products, we have chosen to model co-precipitates as the least expensive process to produce a high concentration of both casein and whey proteins. This process is described below.

Casein, Caseinate and Co-Precipitate Production

Milk proteins have been harvested from milk for centuries through the cheese-making process. Milk coagulates in the presence of rennet enzymes (primarily chymosine) or acid. Casein is first transformed to paracasein which then precipitates in the presence of calcium ions into curds. This process of precipitation by acid or coagulation by rennet is also used to make commercially dried casein. The great majority of casein imports into the U.S. is acid casein and that is the product modeled in this report.

In acid casein production, milk is acidified to the isoelectric point of casein—about pH 4.6—by the addition of of hydrochloric or sulphuric acid. The resulting curd is decanted in a centrifuge and washed and decanted multiple times. The washed curd is then dried, ground and particles separated by sieve prior to bagging. Figure 3 diagrams the process.

Sodium caseinate is the most commonly imported caseinate product into the U.S. It is made by the additional of sodium hydroxide to wet, milled casein curd as described in the process above. The sodium hydroxide is added to bring the final pH to about 6.7 and the slurry is then spray dried. The chief advantage of caseinate over casein is that it is much more soluble.

Because the acid casein or sodium caseinate process is similar to cheese-making, there is also an acid whey stream that can be recovered as a by-product.

Co-precipitate differs from casein and caseinate because it contains practically all of the protein fractions of milk—both casein and whey. The process is similiar to casein production except that time, temperature and pH are carefully controlled to promote interaction between the casein and whey proteins. When the bound casein and whey molecules precipitate, the product is finished like either acid casein or sodium caseinate depending upon the solubility desired in the product.

– 5 –

Figure 3. Process Line for Acid Casein Production. 5

Why Produce Domestic Proteins Now?

MPCs are relatively new dairy products but casein and caseinate products have been manufactured for a very long time. The U.S. produces a small amount of all of the products but has traditionally imported the vast majority. The reason for imports has largely been a matter of price... there were more lucrative dairy products to manufacture domestically with our milk supplies. Several things have changed in the last few years which require us to at least reconsider domestic manufacture of milk protein products.

At least six factors which might impact trade are different today than they were a few years ago. They include:

• Exchange rates

• Transoceanic shipping rates

• World demand for dairy products

• Impact of ethanol

• Changes in climate

• Changes in agricultural policy

– 6 –

5 Dairy Processing Handbook. See TetraPak reference.

Exchange Rates

The United States has a policy of floating exchange rates. This means that the country does not try to hold the value of its currency fixed relative to trading partners. If investors view U.S. investments as good relative to investments in other countries, then the value of the dollar increases relative to the value of other currencies. Since around the year 2000, the dollar has substantially weakened relative to most other currencies. Figure 4 shows the exchange rate relative to the Euro. In 2001, one U.S. dollar would buy about 1.2 Euros. The U.S. dollar has weakened to about half that value.

Figure 4. U.S. Exchange Rates.

Euros per U.S. Dollar

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

2000 2001 2002 2003 2004 2005 2006 2007 2008

If nothing else had changed, imports of dairy products from Europe would look to be about twice as expensive today as they were 6-7 years ago. Conversely, Europeans would see U.S. dairy products as costing about half as much to purchase today. Exchange rates can have a big impact on world trade flows.

– 7 –

Transoceanic Shipping Rates

There are many reasons why shipping rates can change: costs of labor, additional expense of meeting environmental standards, congested ports, etc. Almost certainly one of the major costs of shipping that has increased is the cost of fuel.

This Baltic Exchange in London has calculated an index of the cost of shipping for many years. The Baltic Dry Index is constructed by assessing the cost of 26 shipping routes moving dry commodities via several carriers between major ports. The index is a good proxy for all transoceanic shipping including container ships. Figure 5 shows that the cost of shipping has increased five-fold over just the past two years.

Figure 5. The Baltic Dry Index.6

Baltic Dry Index

0

2000

4000

6000

8000

10000

12000

Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08

The effect of a dramatic rise in the cost of transportation is to produce and consume “locally”. This is a consumer mantra that we are hearing more as gas prices rise in the United States. The same economic pressures hold true globally as well.

– 8 –

6 http://www.investmenttools.com/futures/bdi_baltic_dry_index.htm

World Demand for Dairy Products

The exchange rate for the U.S. dollar could fall because investors are pessimistic about the U.S. economy. It could also fall because investors are optimistic about another countryʼs economy. The U.S. economy has grown at a slow rate for several years but several other countries have experienced particularly strong economic growth.

China has been the “poster child” for economic growth since the turn of the recent century. Strong economic growth in China (8-9 percent GDP for several years) has provided their population with the wherewithal to upgrade the quality of their diet and dairy products feature prominently in that goal. Chinaʼs premier, Wen Jiabao, said that China has set a goal of each child having one-half liter of milk each day. By their own estimation, $1.00 invested in a school milk program will return $2.27 in Gross Domestic Product. The surging Chinese demand for milk and dairy products is partially being met by a dramatic growth in their own milk production (up 39 percent in 2007), but also from increased imports.

Figure 6 shows the cost of a barrel of crude oil. While the increasing cost of oil has had a dampening effect on the U.S. economy (and impacted the exchange rate), it has conversely had a positive impact on the economies of oil exporting countries. Many of these countries have also been significant importers of dairy products. These income effects have globally increased the demand for milk and dairy products.

Figure 6. Texas Sweet Crude Oil Prices per Barrel.7

Crude Oil Prices

$0

$20

$40

$60

$80

$100

$120

1970 1975 1980 1985 1990 1995 2000 2005

$ p

er

Barr

el

– 9 –

7 http://www.economagic.com/em-cgi/data.exe/var/west-texas-crude-long

Impact of Ethanol

The U.S. and the rest of the world have experienced a significant increase in the cost of producing milk. The dramatic increase in the oil and other energy prices is in part being addressed by increased production of bio fuels. In the United States, ethanol production has consumed more than a third of the domestic corn production in 2006-2007. This increased the corn price by more than $1.00 per bushel. Currently, the Chicago Board of Trade is showing the price of corn about double the 2006-2007 prices during 2008-2009. This has had, and will continue to have, a dramatic impact on the global production of milk.

Figure 7. Average Price of a Bushel of Corn–USDA and CBOT 8

Average Corn Price

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

2004 2005 2006 2007 2008 2009

$ p

er

Bu

shel

Changes in Climate

Debates about global warming and climate change have been on-going for several decades. It is safe to say that the majority of scientists no longer debate whether climate change has, or is occurring but rather the extent of the impacts. El Niño effects

– 10 –

8 Blue bars show the USDA prices paid by farms for corn as reported in USDAʼs Agricultural Prices. The red bars show the average Chicago Board of Trade futures prices for corn as of May 2008.

have been taking place with more regularity and with greater impact. An El Niño is a warming of the Pacific ocean and will tend to cause droughts in Oceania where the effect begins and excessive rain on the west coast of the U.S. Australia has been involved in a prolonged drought for more than 5 years. Its milk production has declined by as much as 15 percent from previous levels. New Zealand has experienced more periodic drought during this 5 years but it has had an impact in several years, most recently at the end of the 2007-08 milk production season. The United Nations Intergovernmental Panel on Climate Change (IPCC) has issued a report stating that

“Production from agriculture and forestry by 2030 is projected to decline over much of southern and eastern Australia, and over parts of eastern New Zealand, due to increases in droughts and fires”

Drought in the worldʼs major dairy exporting countries cannot be taken for granted, but it ought to be factored into oneʼs outlook for global milk production.

Changes in Agricultural Policy

Agriculture joined the world trade discussions with the Uruguay round of the General Agreement on Tariffs and Trade (GATT). Signed in 1994, one of the major themes was to reduce agricultural subsidies throughout the multilateral agreement. The European Union had heavily subsidized exports of their dairy products to help maintain strong domestic prices for dairy producers. The so-called “intervention price” was used to subsidize export of products. Through this policy, Europe generally set the tone of world prices for traded dairy commodities.

As the Europe brought other countries into the union, maintaining an intervention price policy became increasingly expensive and was in opposition to the direction of the GATT treaty. The EUʼs Common Agricultural Policy (CAP) reforms of 2005 dropped the subsidy of dairy product exports. This had the effect of lowering the domestic milk prices in Europe but allowed world prices of commodities to rise. Europe seems generally satisfied with the outcome of the CAP reforms and it is unlikely that they will revert to subsidies any time soon.

The collective impacts of exchange rates, transoceanic shipping rates, increased world demand for dairy products, impact of ethanol on production costs and changes in climate and agricultural policy, have all pushed world milk prices much higher and have altered trade patterns for dairy products. The latter half of 2006 and 2007 saw the U.S. export much more skim milk powder than it ever had before. More recently, exports of butter and even cheese have increased. The United States has become a major player in world dairy markets in a very short time. Figure 8 shows the recent increase in the value of dairy product exports from the U.S.

– 11 –

Figure 8. Monthly Value of Dairy Exports from the United States.

Monthly Value of Exports

$0

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

$140,000

$160,000

2000 2001 2002 2003 2004 2005 2006 2007

10

00

s o

f D

oll

ars

MILK AND CREAM AND MILK PRODUCTS OTHER THAN BUTTER OR CHEESE

BUTTER AND OTHER FATS AND OILS DERIVED FROM MILK

CHEESE AND CURD

Objectives of the Study

Because many economic factors have changed in the last 5-7 years, we were asked to assess the potential to:

1. Produce protein products domestically that were historically imported (casein and caseinate)

2. Produce protein products domestically that are relatively new (MPCs)

3. Produce these protein products for export

Toward those objectives, we have chosen to model the five protein products detailed above in a dynamic framework to assess a myriad of market impacts over time. We will also look at the individual and collective impacts of the six economic factors that we believe have changed (exchange rates, energy, global demand, supply, etc.).

– 12 –

Dynamic Dairy Sector Model

The dynamic model developed for this study is an extension of a previous detailed dairy industry model that has been used to assess the market impacts of promoting whey protein products (Nicholson and Stephenson, 2006) and generic advertising for fluid milk and cheese (Nicholson and Kaiser, 2008). This model builds upon a previous conceptual commodity model (Sterman, 2000) and the dairy industry price determination model in Nicholson and Fiddaman (2003). The model includes a total of 17 final and intermediate dairy products. In most dairy-related models, intermediate products are those dairy products used in the manufacture of other dairy products. A common example is the use of dried milk in cheese manufacturing. Final products are those used by non-dairy manufacturers (e.g., other food processors) or final consumers. Perishable products such as fluid milk, yogurt and ice cream are flow variables for which production is equal to sales. Commercial inventories of storable commodities such as butter, cheese, dried milk and dried whey (used in the minimum pricing formulae) are stock variables, where production increases inventories and sales reduce them. Increases in commercial inventories of these products result in decreases in the prices of these products. In the dairy industry, manufacturers separate raw milk into a variety of components (butterfat, proteins, lactose and minerals) using various physical processes (e.g., filtration). As a result, dairy models should adequately represent the physical balance of these components across different product uses. The model includes the use of skim milk and cream components to capture component balance. Essentially, cream represents fat and skim milk represents protein, lactose and minerals.

The dynamic model includes minimum pricing regulation, assuming that most milk is regulated under the classified pricing and pooling system associated with Federal Milk Marketing Orders. In addition to the minimum regulated pricing, other key policy interventions include price supports for selected manufactured products (butter, cheese and dried milk) and restrictions on dairy product trade for many products. Price supports operate through the willingness of the Commodity Credit Corporation (CCC; established by the federal government) to purchase dairy products at prices designed to maintain a minimum level of milk prices paid to dairy farmers, or through direct payments made to farmers when milk prices fall below a target level (MILC). Dairy trade policies restrict the quantities of many imported (storable) products, which historically (but not recently) has maintained US milk and dairy product prices above those in international markets.

Farm milk supply response occurs through changes in both productivity (milk production per cow per year) and changes in the number of cows. Productivity responds with a delay to changes in the price of the raw input (milk) in the short run (complete response to a step change in the milk price relative to a reference price occurs within about 3 months), whereas the number of cows responds to an exponential smooth (another delay) of relative net margins over two years. The number of cows results from a biological reproduction rate (assumed to be constant) and the rate of removal of animals from the aggregated national herd, which depends on the profitability of milk production.

– 13 –

The rate of removal is the inverse of the average animal lifetime, which is a function of current margins relative to long-term expectations. The degree to which dairy farmers modify average animal lifetimes in response to relative margins is unknown, so the impacts of this parameter on simulated outcomes are evaluated with sensitivity analysis.

The model is formulated as a system of nonlinear differential equations solved by numerical integration. Parameter values for behavioral responses to prices and inventory levels and the initial stock values are from previous dairy industry models (Bishop and Nicholson, 2004; Nicholson and Fiddaman, 2003), data from the Agricultural Marketing Service of the US Department of Agriculture (which administers the minimum pricing regulations), and an extensive network of industry contacts (Cornell Program on Dairy Markets and Policy, 2006). The base year for the model is 2006; values of milk production, dairy product demand and trade are based on data from that year. The model simulates all variables at monthly time intervals over the six-year period 2006 to 2011 using a small calculation interval to approximate a continuous-time system. Model evaluation followed the process Sterman (2000) describes. A previous version of the model evaluated the impact of growth in dairy product demand on dairy farmer revenues (Nicholson and Stephenson, 2006). For the purposes of the analyses herein, underlying growth in demand for dairy products other than protein products (i.e., outward shifts in the demand curves) in response to increases in household income and population are exogenous, and are from Nicholson and Stephenson (2006) and Schmit and Kaiser (2006).

Additional Model Components for Protein Products

The extension of the previous model involves the addition of protein product manufacturing and demand, product prices and profitability. The characteristics of this section of the model are as follows:

• 5 product categories: MPC42, MPC70, MPC90 9, Casein, Caseinates, where each of these categories represents a somewhat broader range of products.

• Two regions: US and the rest of the world (ROW)

• Endogenous protein production capacity (which depends on expected profitability)

• Inventories by product and region

• Intermediate (i.e., other dairy product use) and final demand for protein products

• Final product demands respond to own and cross-price elasticities

• Fixed proportions intermediate product demands for use in cheese, yogurt, ice cream and casein for caseinate production.

– 14 –

9 This is modeled as made through a co-precipitate process.

• Component balancing (skim milk and cream are basic fractions; fixed proportions yields of products based on initial raw milk composition)

• By products included: separated whey and whey cream from casein production, UF permeate from MPC production

• Revenues include protein product and by-product sales

• Costs include milk input costs (modified by FMMO obligations in case of US), processing costs, surplus whey and permeate disposal, whey separation (as appropriate)

• Reference value structure used to initialize model in dynamic equilibrium (where variables are initialized at average 2006 values and do not change with time; this serves as a convenient benchmark for subsequent analyses)

Protein Product Capacity

The software used to for this modeling effort is Vensim (Ventana Systems, Inc, 2005). Vensim uses as series of iconic “views” that depict the structure (variables and relationships) which comprise the model. The Protein Product Capacity “view” is based on the capacity structure from Sterman (2000) and has been employed for other dairy products in previous modeling efforts. The core is what is called a “stock management structure” with a two-stock supply chain for production capacity. The two stocks include a current level of processing capacity and capacity “on order”, that is, in the process of being built and brought online (Figure 9). The amount of capacity depends on the rate at which capital is brought online and the rate at which is it discarded. The rate at which capacity is added depends on the rate at which capacity is ordered and the length of the delay involved between ordering new capacity and having it become available. Discards are assumed to take place proportional to the amount of capacity currently online, based on an average lifetime of processing capacity of 20 years.

The amount of capacity ordered depends on the current level and a “desired” level of capacity. The desired capacity depends on long-term expected ratio of price to costs relative to a reference ratio of price to costs. If processing has become more profitable as indicated by an increase in the price to cost ratio, there will be an increased demand for processing capacity. The profitability in this case is measured by the proportional difference between the expected and reference long-run price to cost ratio, as follows:

Proportional Difference in Ratio = [Ratio P to C – Reference P to C Ratio] / Reference P to C Ratio

– 15 –

Figure 9. Protein Product Capacity and Investment.

If the long run expected ratio increases, the proportional difference will increase. The effect of a change in this proportional difference on the desired processing capacity is given by a nonlinear lookup function (Figure 10). This value of this “effect” function is multiplied by the current capacity to determine a “desired” level of protein product capacity. Thus, if the Expected LR Margin increases, there will be an increase in the desired level of processing capacity, which will be reflected in increased orders of processing capacity, but which will come online with a delay. In addition to this endogenous structure (which would tend to expand protein product manufacturing capacity in a steady continuous manner if profitable), we also structured the model to allow for rapid “step” changes in capacity to assess the impacts of investments on the scale of plants processing 3 million pounds of raw milk per day.

Protein Product Inventory, Pricing, Margins, Federal Order Obligations, and Trade View

This view includes most of the key calculations related to protein product inventories, prices, profitability and trade. Similar to previous dairy industry models, it uses product inventories (in this case, by region) to drive prices and profitability. Inventories reflect the current amount of protein products in storage in the two regions (Figure 11). Production increases inventories, and sales (domestic and export) reduce them. Protein product production is a function of capacity times utilization, where capacity is as described in the previous paragraph and utilization depends on current profitability of processing relative to a reference value. Protein product production in turn determines input requirements (e.g., milk) and by-product production (whey or permeate; Figure 12). Sales are determined based on the quantities demanded of each product in each region for final (i.e., non-dairy) use and intermediate (i.e., other dairy product) use (Figure 13). Final demand quantities are functions of “own” prices (the price of the product itself) and “cross” prices (the prices of other products that are complements or

– 16 –

substitutes). Intermediate demand is currently in fixed proportions, which means that demand for the protein product as an input for manufacture of another dairy product responds to production of another product (e.g., cheese) and so is only indirectly affected by protein product price changes. Structure has been added to allow assessment of various changes in the demand for protein products, such as constant annual growth rates. A similar approach was used to assess growth in demand for whey products (Nicholson and Stephenson, 2006).

Figure 10. Relationship between Long-Term Proportional Difference in Price to Cost Ratio and Multiplier for Desired Protein Product Capacity

Product inventories are compared to current domestic and export sales rates to determine Inventory Coverage (Figure 11). Inventory Coverage (IC) influences prices: if IC is below a desired number of months (which indicates that inventories are deemed to be insufficient), product prices increase. The amount of the increase is determined by the level of inventories and a parameter representing the sensitivity or prices to IC. An increase in prices reduces the quantity demanded (ultimately reducing sales) and increases net margin (other things being equal), which will increase utilization (Figure 12) in the short term and capacity in the long term (Figure 9), bringing inventories closer to desired levels. Conversely, if IC is above the desired number of months, inventories are deemed to be too large, and product prices will decrease. A price decrease increases the quantity demanded and reduces profitability, utilization and capacity to move inventories back toward the desired level.

– 17 –

Figure 11. Protein Product Inventory and Pricing Structure

Profitability of protein product processing is represented by net margin, that is, by revenues less costs. Revenues to protein processing enterprises can include protein product sales, cream (and sometimes whey cream) sales, sales of whey (or separated whey) and sales of permeate when filtration processes are used to make protein products (Figure 14). Costs include raw milk costs (or other protein products used as inputs, such as casein in caseinates), protein product processing costs, permeate (processing and disposal) costs for products made with ultrafiltration, and whey separation (when whey cream is separated). The calculations are most complicated for the milk component input costs. For the US, milk component input costs assume a large proportion 10 of the components used in protein product processing are priced under Federal Orders. Accordingly, Federal Order obligations are calculated based on the use of components from skim in Class IV products; cream is assumed to be separated and made available to other productsʼ production (e.g., butter, ice cream). The unit milk input cost includes over-order premiums and a discount to the value of milk not pooled. For ROW, a milk price is assumed 11 and this is used to calculate milk input costs.

– 18 –

10 This proportion, however, is a parameter in the model and can be varied.

11 This assumption is made to avoid explicitly modeling all elements of the dairy sector in other protein-product processing countries. This milk price can be varied to assess its impact on protein product processing in the US and ROW.

Figure 12. Production, Utilization, Milk Input Demands and By-products of Protein Product Processing

The US imports large quantities of protein products and both its domestic and export sales represent a very small market share. Several model versions were explored that explicitly allowed the US to both import and export, but the very small market share created calculation issues and instabilities for these model versions. Thus, we ultimately determined to make the simplifying assumption that US production of protein products would serve domestic markets rather than export markets. We assumed that any US domestic protein products produced would be sold in US markets and imports from the rest of the world would be calculated as total US demand less sales by US manufacturers (Figure 15). We also assumed that the US price for protein products would be the price in ROW plus transport costs and adjusted for any changes from a 2006 base exchange rate between the US dollar and the NZ dollar. That is, the US price is calculated as:

– 19 –

where EXRATE is the number of $US required to purchase one unit of foreign currency and TC is the transportation and other costs of shipping the product from the ROW to the US (expressed in $US). Additional US production and domestic sales, however, will modify ROW prices because the US is a major importer of protein products. Thus, an increase in US production will reduce US imports, decrease the quantity demanded of protein products produced by the ROW, increase inventories in the ROW, and decrease product prices in world markets (at least in the short run). In the longer term, lower prices will increase the quantity of protein products demanded and US imports may increase.

Figure 13. Final and Intermediate Demands for Protein Products

– 20 –

Figure 14. Protein Product Revenues, Costs and Net Margin

Figure 15. Protein Product US Import Demand and ROW Export Sales

– 21 –

Static Profit Analyses

Most of the scenarios contained in this report are dynamic market results generated with the model. In other words, the dynamic model is initialized with 2006 base year values and runs forward month-by-month generating expected outcomes from the previous monthʼs calculated outcomes. As a first step to examine the profitability of producing protein products, we used the model as a static calculator of incomes and costs with actual values for a few time periods. The time periods evaluated included: average prices and quantities for 2006, average prices and quantities for 2007 and average prices and quantities for February 2008.

Costs of Processing

An important unknown value in the modeling effort is the cost of processing the five protein products. There have been a few estimates of the cost of processing a few of the products of interest. In particular, the U.S. Trade Commission Report and a study by Bishop and Nicholson published their estimates in 2004. We have conducted cost of processing studies for several products over the years (most recently, Stephenson 2006) using various techniques including plant surveys and economic engineering. Estimates of processing costs for this project were made with a combination of techniques.

There are a few low concentration MPC plants operating in the U.S. A few years ago, we had undertaken a survey of those plants to determine the achievable costs at that time. However, for products like acid casein and caseinate, we were unable to survey plants in other countries and were unable to find recent published cost estimates from other sources. We estimated these costs using a combination of methods.

A first approach was synthetic economic engineering. Casein and caseinate production has similiarities with cheese production in that it begins with curd production and has a whey stream as a by-product. With good and recent experience in cheese cost of processing, this was used as a starting point and the costs of drying, grinding, sieving and bagging the curd was estimated.

A second approach was econometric estimation of costs from prices. The simple idea is that if we know something about a countryʼs cost of milk as an input, the costs of transporting the finished product to the U.S., and the exchange rate between the two countries, we should be able to estimate the implied costs of processing if we assume a profit margin for the product. This was done for acid casein production in New Zealand.

The synthetic economic engineering and the econometric estimation were used to derive an estimated cost of processing acid casein in the U.S. at 20¢ per pound in 2006. Table 2 shows the estimates of processing costs used in the model for the five products as well as the cost estimates for some of the products from the 2004 USITC publication and the Bishop, Nicholson publication.

– 22 –

Table 2. Estimates of Protein Product Processing Costs per Pound.

MPC 42 MPC 70 MPC 90Acid

Casein Caseinate

USITC Report 19 - 24¢ 29 - 34¢ 35 - 40¢* 20¢ —

Bishop & Nicholson 29¢ 104¢ — 13¢ —

Estimates Used in the Modeling 24¢ 34¢ 30¢ ^ 22¢ 24¢

* Estimate was for an MPC 80^ Estimate using a co-precipitate process

Figure 16. Static Profit Estimates for Protein Products and NDM, 2006.

0

100

200

300

400

500

600

700

800

900

1000

NFDM MPC 42 MPC 70 MPC 90 Casein Caseinate

Cen

ts p

er lb

Pro

tein

Pro

duct

By Product Revenues

Protein Product Price

Total Unit Costs

– 23 –

Figure 17. Static Profit Estimates for Protein Products and NDM, 2007.

0

100

200

300

400

500

600

700

800

900

1000

NFDM MPC 42 MPC 70 MPC 90 Casein Caseinate

Cen

ts p

er lb

Pro

tein

Pro

duct

By Product Revenues

Protein Product Price

Total Unit Costs

Figure 18. Static Profit Estimates for Protein Products and NDM, February, 2008.

0

100

200

300

400

500

600

700

800

900

1000

NFDM MPC 42 MPC 70 MPC 90 Casein Caseinate

Cen

ts p

er lb

Pro

tein

Pro

duct

By Product Revenues

Protein Product Price

Total Unit Costs

– 24 –

Figures 16 - 18 display the results of the static profit analyses for average 2006 and 2007 values and for February 2008. The dark green area of the charts indicates the price received (paid) per pound of the protein product. The lighter green area shows the by-product revenues associated with the production of a pound of the protein product. These by-products include sweet cream for all products, lactose for the MPCs, and dried whey and whey cream for the casein and caseinate products. The sum of the product prices and the by-product revenues constitute a total revenue per pound of production of the protein product. The red bars indicate the total costs associated with the production of a pound of protein product. Those total unit costs include the cost of processing in Table 2, the regulated and over-order premium costs of the milk needed to produce a pound of the protein products, and the cost of processing the by-products.

Several observations can be made for Figures 16 – 18. The revenues for producing nonfat dry milk and cream are basically covering the costs of processing and the farm milk purchased to produce the products. This is not surprising as product price formulas for manufacturing milk calculate the farm milk price from surveyed prices of nonfat dry milk and butter sales. Profit for any manufacturer of NDM is dependent on having manufacturing costs below those used in the formulas.

Profitability for the other protein products is dependent upon the price relationships of the milk used and the products produced and these relationships do change over time. In February 2008, our estimation is that it would have been profitable to produce any of these five products in the United States. However, the MPC 90 would not have been profitable to produce in either 2006 or 2007 and MPC 70 would have had quite thin profit margins in those years. And, in general, lower concentration MPCs, casein and caseinates production in the U.S. would have been profitable in any of these time periods. Also, it is seldom the case that the sale of any product would generate profit by itself—by-product revenues are crucial to overall profitability of these plants.

Estimated Market Size and Plant Numbers

Another important dimension of the decision to invest in additional processing capacity for domestic production of protein products is the current and potential size of the market. Because the US imports the vast majority of its protein products, the current US market size can be estimated by the quantity of imports (Table 3). Based on the US market share of these products in 2002 (USITC, 2004), a rough estimate can be made for global demand of each protein product. The size of the market is important because it provides an indication of the number of plants that are required to supply the US market. Although a number of what would currently be considered large plants (around 3 million lbs milk per day capacity) may be built, the average size might be smaller. We calculated the number of plants with capacity of 2 million lbs per day required to produce sufficient quantities of each protein product to satisfy estimated US demand in 2006 (Table 3). The number of plants required ranges from one to nine, being smallest for MPC42 and largest for casein, consistent with the quantity of the products demanded and the amount of raw milk required to produce a pound of protein product.

– 25 –

Given the relatively small number of plants required to satisfy US demand in each case, it would not be surprising that if a number of large plants came on line within a short period of time, US protein product prices could be adversely affected. Our subsequent analyses with the dynamic dairy sector simulation model support this proposition. This suggests that those companies that invest in capacity first are likely to be more profitable than those entering production later, but also that over-investment brought on through a lack of coordination among dairy manufacturing companies could undermine the profitability of the products.

Table 3. Estimated Market Size and Number of Plants Required to Supply the US Market for Protein Products

Units MPC42 MPC70 MPC90 Casein Caseinate

Market Size

US Imports, 2006 mil lbs 38.4 77.9 22 111.1 67.4

US Percentage of World Market1 % 65 65 65 37 37

Estimated Global Market mil lbs 59 119.8 33.9 300.1 182.1

Plant Numbers Calculations

lbs raw milk per lb protein product2 lbs/lb 14.2 26.4 36 39.7 40.2

raw milk required for US market mil lbs 545 2,056 794 4,409 2,709

Average capacity of plant mil lb/day 2 2 2 2 2

Number of plants for US market3 Plants 1.1 4.1 1.6 8.8 5.4

1 This share is estimated based on 2002 data from USITC (2004). Hence, global market size may be only a rough estimate if US market share has changed substantially

2 Varies with raw milk composition. Calculations are based on US average test in 2006.3 Assumes plant running at 80% of capacity of 2 million lbs per day, 6 days per week

Dynamic Modeling of Programs to Provide Incentives for Increased US Production of Protein Products

The model structure also includes variables and relationships to represent programs that would provide incentives for US manufacture of protein products. These programs include:

• A per unit production subsidy (for example, $0.10/lb product produced)

– 26 –

• A risk reduction program that would pay US protein product manufacturers the difference between returns earned on skim milk processed into NDM and returns earned on skim milk processed into protein products 12

• A risk reduction program that would pay US protein product manufacturers a certain proportion of the difference between a target price for protein products and the current US market price

• Contributions to the funds required for investments in protein product manufacturing plants

• Interest rate subsidies for borrowing required for investments in protein product manufacturing plants

The focus of the analysis in this report is on the first two of these options, given that protein product manufacturing currently appears profitable for most products under recent market conditions. In addition, previous model versions examined the use of export subsidies for US-manufactured protein products. Analysis of export subsidies is not included in this report for two reasons. First, the economic logic of export subsidies is not strong given the small US market share. Use of export subsidies to any significant extent would lower world market prices, resulting in incentives for increased imports from the rest of the world to the US. Preliminary analyses with previous model versions including US exports suggest that export subsidies are not cost effective when the US has a small share of domestic and world markets. The second (and more practical) reason is that the current model has simplified the structure of protein product sales in the US so that increases in US protein product output would go entirely for domestic sales. Analysis of export subsidies under this assumption is not possible.

Reference Calculations Views

Additional views contain reference calculations for federal order obligations based on the use of components for the reference (baseline) quantity of production and reference net margin calculations based on the revenues and costs associated with the production of the reference quantities. Both are part of the structure used to initialize the model in dynamic equilibrium, but are also inputs into the calculation of costs and revenues.

– 27 –

12 Because cream revenues from NDM and protein products per unit raw milk entering the plant would be the same, the risk reduction payment per unit raw milk processed would be (NDM Price-NDM Unit Processing Costs)*Units NDM per unit Raw Milk minus (PP Price – PP Unit Processing Costs)*Units PP per unit Raw Milk, only when the NDM-related value is larger. This calculation ignores other by-product revenues for protein products, which are assumed to be small in our analysis.

Figure 19. Incentive Programs for US Protein Product Manufacture

Model Analyses and Scenarios

In addition to the “static” profitability analyses reported above, the dynamic dairy sector model is used to undertake three different types of analyses related to the profitability of protein product manufacture and the market impacts of increasing the demand for nonfat milk solids. These analyses are:

• An assessment of the profitability of US protein product manufacturing under world market conditions observed during 2006 to 2008 and projected future conditions;

• An assessment of incentive programs to encourage investment in protein product manufacturing capacity under recent world market conditions;

• Examination of market impacts of investments in protein product manufacturing capacity under 2006 market conditions.

Each of these types of analyses is described in additional detail below.

– 28 –

Assessment of Profitability under Observed World Market Conditions 2006 to 2008 and Possible Future Conditions

For this assessment, prices for NDM and butter observed in the US market during January 2006 to February 2008 are assumed, along with a shortage of milk in world markets. This combination results in the higher protein product prices observed in world markets during 2007 and early 2008. Other factors that would influence the sales potential and profitability of US protein product manufacture include increases in transport costs for products imported by the US, the depreciation of the US dollar versus most other major currencies, and the potential for growth in demand for diary protein products in world and US markets. Specific assumptions for these analyses include:

• An increase in transportation costs from $1.5/cwt (of finished product) to $3.5/cwt during the period July 2006 to December 2007, based on increases in the Baltic Dry Index

• A 20% decrease in the value of the US dollar relative to the New Zealand dollar during the period 2006 to December 2007, based on observed exchange rates during that period;

• Growth of 3% per year for both US and ROW demand for each of the protein products except casein (for which 2% per year was assumed) beginning in January 2007;

• Use of US NDM and butter prices to calculate the raw milk input cost for protein product manufacturers during January 2006 to February 2008, and small increases in the milk price to reflect a long term expected average Class IV price of $15.87 through January 2012.

• Shortfalls of milk available for protein product manufacturers in the ROW, as indicated by costs of their milk input. Key factors in this include reductions in CAP support for milk production in the EU in recent years, climatic conditions in locations such as Oceania, and growing global demand for milk and dairy products. A key assumption concerns how long the global milk shortfall will persist. Our baseline assumption is that global milk production relative to demand levels will make a gradual recovery to something like 2006 levels by January 2010. However, because this depends on a variety of highly unpredictable factors (such as climate) we conduct sensitivity analyses on the timing and extent of world milk supply recovery.

• We use protein product prices observed in early 2008 and adjust them to likely initial values in 2006 based on relative protein content of these products and NDM. This assumption is necessary because there are no formally reported time series data for production and prices for this information. In addition, there are

– 29 –

limited data available about world production and sales of protein products. 13 The size of the global market was estimated based on US imports of protein products in 2006 and the market share of the US in total global production in 2002 from USITC (2004).

• US demand for these products is assumed to be imports plus exports, as reported by the US Census Bureau, with the data obtained through the USITC Dataweb (dataweb.usitc.gov). Intermediate demand is based on estimated aggregate use of these products in other dairy product manufacture, using information from detailed surveys of protein product users conducted by the US ITC in 2003 (USITC, 2004). Final product demand is calculated as a residual and is depends on the price of the product and the prices of other protein products. US capacity as of January 2006 is assumed based on the assumption that the US currently has a quite small market share for each of these products (1-2%).

Based on these assumptions, we assess the contribution of each of these factors (transport costs, exchange rates, demand growth and milk supply and demand conditions) to the currently observed prices and assess how the profitability of protein product manufacture in the US will evolve over time. These analyses are done for a baseline that represents our best guess about global milk supply recovery, and sensitivity analyses that allow us to examine alternative assumptions about global milk supplies.

Assessment of Incentive Programs to Encourage Investment in Protein Product Manufacturing Capacity under Recent and Possible Future World Market Conditions

Given the foregoing, this set of analyses uses the same underlying assumptions, but also examines the impacts of production subsidies and a risk reduction program that would guarantee US protein product manufacturers a return on raw milk processed equal to the return that could be earned if that milk was processed into NDM. In addition, we examine the impact that rapid increases in US protein product manufacturing capacity would have on prices and profitability.

One limitation of the current version of the dynamic dairy sector model is that it assumes that manufacturers respond in a smooth, continuous way to the profitability of domestic protein product manufacture relative to 2006 levels, slowly increasing capacity over time if production is profitable. In reality, however, investments in plants are “lumpy,” and this is particularly true given that the size of the market in the US indicates that relatively small numbers of large-capacity plants could supply the entire US market. To more adequately assess the impacts of rapid increases in US capacity, for scenarios

– 30 –

13 This limitation should be rectified to enhance abilities to assess market potential.

that address this issue we assumed that one plant for each product type 14 with the ability to process 3 millions lbs of raw milk (2.7 million lbs of skim) per day come online simultaneously in January 2009. Because the profitability of production also affects the utilization of the capacity and the assumed baseline utilization is 80% of capacity, US production does not immediately increase by the total amount of capacity added. Addition of capacity that substantially increases US protein product supplies leads to a reduction in the market prices for the products in the absence of rapid demand growth. In addition, increased capacity initially reduces the amount of “residual” skim milk available for processing into NDM, which will increase NDM prices and the relative profitability of NDM manufacture, at least in the short term. In addition to the assumptions about capacity, we examined scenarios that assumed:

• A production subsidy. A $0.10/lb subsidy on production for each product beginning in January 2009 and operated through January 2012. This is admittedly simplistic, in part because the subsidy is not linked to the profitability of the products (and hence, the implied need for a subsidy), the start-up period for the plants themselves, or the differences in processing costs. However, it provides a starting point for the discussion of the impacts of production subsidies on profitability;

• A risk reduction program. Under this risk reduction program, protein product manufacturers would be paid the difference between the calculated returns from processing raw milk into NDM and cream and the calculated returns from processing raw milk into protein products, cream and other by-products. Because of the rapid changes in the relative prices of NDM and protein products during 2006 to 2008, this program is assumed to have begun operating in January 2006. Our analyses thus assess how this program would have affected profitability during that time period.

The key variables to be examined for these analyses are the profitability of US protein product manufacture and the total costs of the programs.

US Market Impacts of Investments in Protein Product Manufacturing Capacity and Investment Incentive Programs under 2006 Market Conditions

Because the previous analyses assumed the milk input costs that would have been observed during 2006 to 2008 for US protein product manufacturers, they do not address directly the question of the larger market impacts, for example, impacts on

– 31 –

14 For MPC42, one 3 million lb per day plant would provide more than double the estimated US demand for this product. Thus, we assumed the addition of capacity sufficient to supply all of US demand for MPC42, which is a plant processing approximately 1.5 million lbs of raw milk per day (see Table 5).

other dairy product prices, class prices and the all-milk price paid to farmers. 15 Thus, we developed a series of analyses that use 2006 as a base year and make changes, particularly to the US manufacturing capacity for protein products. These analyses provide an indication of the types and sizes of market impacts and the effects of programs to provide incentives to US protein product manufacturers. To undertake these analyses, we assumed the average values for 2006 as a starting point, then modified various factors (a subsidy program, a risk reduction program, and capacity) in combination beginning in January 2007. The choice of January 2007 as a starting point is arbitrary, but this was chosen to allow for as much time as possible after the change to be simulated with the model. It is worth reiterating that these changes do not reflect the changes observed in 2007 and subsequent years. They are more appropriately interpreted as “controlled experiments” with the model to assess how various changes would have played out compared to the situation in 2006.

More specifically, the scenarios analyzed included:

• Production Subsidy. Providing a unit production subsidy for all protein products of $0.10/lb beginning in January 2007;

• Increased Capacity. As for the previous analyses, on plant of each type with a capacity to process 3 million lbs of raw milk per day were brought online in January 2007;

• Increased Capacity with Risk Reduction Program. Combines the increased capacity brought online in January 2007 with the risk reduction program previously described that guarantees a return on skim milk for protein product manufacturers equal to that for NDM, also assumed to be implemented in January 2007;

• Increased Capacity with Production Subsidy. Combines the increased capacity brought online in January 2007 with the production subsidy program described above.

– 32 –

15 The current version of the dynamic dairy sector model was not able to generate the full extent of price variation observed in US markets during 2007 and 2008 based on assumptions about changes in milk production, domestic demand, international prices for dairy products and demand for US product exports. This was particularly true for NDM prices, which the model predicted would only have reached about $1.60/lb during late 2007. This was the motivation for assuming the underlying NDM and butter prices in the previous analyses rather than letting the full dynamic model generate them. The use of these assumptions, however, implied that the impacts of these price changes on other dairy markets and impacts on all-milk prices could not be predicted by the model.

Key impacts to be assessed under these scenarios are changes in:

• Prices of protein products;

• Unit profitability of protein products for US manufacturers

• Profits earned by US protein product manufacturers

• The all-milk price paid to dairy producers;

• Classified milk prices, particularly for Class IV

• Changes in cumulative dairy farmer revenues, particularly those due to programs

• Cumulative costs of implementing the various programs and their benefit-cost ratio

Results: Assessment of Profitability under Observed World Market Conditions 2006 to 2008 and Possible Future Conditions

As indicated above, a number of factors have influenced the prices of protein products in US and world markets, which in turn will influence the profitability of protein product manufacturing. Our analyses indicate that for each of the five categories of protein products, the largest component of change has been world market conditions (Figures 20 through 24). In each figure, four scenarios are presented to represent a build up of these factors. The pink line labeled transport indicates the impact of changing transport costs on protein product prices. The yellow line labeled Growth Transport indicates the combined effects of increased transportation costs and demand growth. In each case, the effects of these changes are to increase protein product prices, but by relatively small amounts. The impact of the rapid depreciation of the US dollar is shown by the light blue lines, which also include the effects of growth and changes in transport costs. Thus, the depreciation of the dollar has lead to price increases in protein products, particularly during the period from June 2006 to the end of 2007. The combined effects of transportation costs increases, demand growth, exchange rate changes and milk supply shortfalls are indicated by the dark blue line labeled “all”, which projects protein product prices in early 2008 similar to those observed. After reaching a peak during 2008, the price of each protein product is projected to decline based on our assumptions about the rate and extent of recovery in world milk production.

– 33 –

Figure 20. Model-Predicted Components of Changes in Price for MPC42, 2006 to 2012

Figure 21. Model-Predicted Components of Changes in Price for MP70, 2006 to 2012

– 34 –

Figure 22. Model-Predicted Components of Changes in Price for MPC90, 2006 to 2012

Figure 23. Model-Predicted Components of Changes in Price for Casein, 2006 to 2012

– 35 –

Figure 24. Model-Predicted Components of Changes in Price for Caseinate, 2006 to 2012

What has been the impact of these predicted changes in prices on the estimated profitability of protein product manufacture? Our analyses suggest that during the first two years of the simulation, profitability fluctuated rapidly month by month, and was often negative during mid-2007 (Figure 25). One main reason for the negative profitability estimates was that NDM and the butter prices in the US increased rapidly during this time period, increasing the Class IV milk cost and the unit milk input cost for US protein product manufacturers. In essence, the milk input costs are estimated to have risen more rapidly during 2007 than did prices of protein products, so profitability was reduced. As protein product prices continued rising and NDM and butter prices moderated late in 2007 and early 2008, protein product manufacture was estimated to become more profitable. The model predictions based on our assumptions about recovery of world milk supplies over the next four years suggest that three of the five protein products (MPC42, Casein and Caseinates) will remain profitable during this time period. MPC70 is estimated to be profitable for about two years during this time period, and MPC90 is estimated to be profitable only briefly (again, based on our assumptions about milk input requirements and product prices).

– 36 –

Figure 25. Predicted Unit Profitability for US Protein Products, 2006 to 2012

Because the foregoing results depend to a great extent on assumptions about future developments in world milk production, it is appropriate to undertake sensitivity analyses to assess the likely ranges that may be observed for protein product prices and profitability. For these analyses, we made a range of alternative assumptions about how quickly and to what extent world milk supplies will recover. These assumptions ranged from full recovery by the end of 2008 to full recovery by the end of 2011. The average price of manufacturing milk in New Zealand in 2006 was $15.11/cwt ($US). The sensitivity analyses assumed that after complete adjustment of world supplies this price could be as low as $14.63 and as high as $20.58. US Class IV milk prices were assumed to range from $15.02 to $16.02. As might be expected, the wide range of possible world and US milk prices results in wide ranges of predicted protein product prices and profitability of manufacturing (Figures 26 through 34). However, the results suggest that even for the lowest world milk prices and highest US Class IV milk prices, a number of the protein products are likely to remain profitable. Consistent with the previous results for our baseline scenario, the model predicts a high probability the MPC42, casein and caseinates will remain profitable under alternative assumptions about US and world milk prices. MPC70 has about equal probability of being profitable from 2008 through 2011, and MPC90 is predicted to be profitable only under continued shortfall in world milk supplies and the lower range of assumed US Class IV prices.

– 37 –

Figure 26. Sensitivity Analysis for Prices of MPC42

Figure 27. Sensitivity Analysis for Unit Profitability of MPC42

– 38 –

Figure 28. Sensitivity Analysis for Prices of MPC70

Figure 29. Sensitivity Analysis for Unit Profitability of MPC70

– 39 –

Figure 30. Sensitivity Analysis for Prices of MPC90

Figure 31. Sensitivity Analysis for Unit Profitability of MPC90

– 40 –

Figure 32. Sensitivity Analysis for Prices of Casein

Figure 33. Sensitivity Analysis for Unit Profitability of Casein

– 41 –

Figure 34. Sensitivity Analysis for Prices of Caseinate

Figure 35. Sensitivity Analysis for Unit Profitability of Caseinate

– 42 –

Results: Assessment of Incentive Programs to Encourage Investment in Protein Product Manufacturing Capacity under Recent and Possible Future World Market Conditions

Using the baseline values for world milk supplies and US milk prices through 2011, the dynamic dairy sector model can be used to assess the impact on protein product prices and profitability of two alternative programs (subsidies and risk reduction) to provide incentives for US manufacturers. As mentioned above, these two program are evaluated assuming implementation of the subsidy in January 2009 with and without rapid increases in capacity in that same month for all protein products. The risk reduction program is assumed to be implemented in January 2006 to examine the impacts on profitability during periods of negative profitability predicted during 2007. A constant per unit subsidy for US production would have the impact of increasing the profitability of US protein product manufacture, but by somewhat less than the amount of the subsidy. This “somewhat less” is due to the fact that enhanced profitability will provide incentives for increased production, thereby resulting in somewhat lower protein product prices (Figures 36 through 40). Prior to the additional capacity coming online, the risk reduction program has the desired effect of reducing the losses predicted by the model in the absence of the program during mid-2007. This effect is particularly notable for MPC70 and MPC90, but is present for the other three protein products as well. Thus, the pink line is above the blue line during the trough in profitability during 2007 due to the effect of the program.

Increases in capacity are predicted to have marked impacts on profitability in both the short and long term. Although not shown in each of the figures for clarity, the effect of adding capacity alone is qualitatively similar in each case to the line labeled “capacity risk” (except for MPC90). Adding capacity increases protein product supplies (sometimes by rather large amounts compared to the size of the US market) resulting in rapidly lower product prices and lower net margins. Product prices are predicted to recover somewhat during the latter part of 2009 due to reductions in capacity utilization in the rest of the world (ROW) due to lower profitability there brought on by lower product prices. As might be expected, price decreases are smaller for those products for which the additional of one US plant to production of the product is a smaller share of the total US market. Thus, one MPC42 plant has a much larger influence on prices and profitability than the addition of one casein plant.

The subsidy and the risk reduction program increase the profitability of protein product manufacture when capacity is added in the US, compared to the level of profitability that would have happened in the absence of the programs. However, this does not imply that the programs make the products profitable, as can be seen from the figures. Except for MPC90, the subsidy program results in a higher degree of profitability than the risk reduction program, primarily because the subsidy program is assumed to operate continuously during January 2009 to January 2012 and the risk reduction program only provides payments when the profitability of protein product manufacture is low. For MPC90, the program is in continuous operation at a high level of payment

– 43 –

throughout the model simulation period, which markedly enhances the profitability of MPC90 production relative to the subsidy program (Figure 38).

The predicted cumulative costs of implementing the subsidy and risk reduction programs during the modeled time periods vary by product (Figures 41 through 45), ranging from a high of nearly $120 million for the risk reduction program for MPC90 to less than $100,000 for the risk program for MPC42 (Table 4). In general, the subsidy programs operating under the assumption of capacity expansion result in continuous increases in, and higher cumulative amounts for, program costs. This occurs, again, because they would be making payments to protein product manufacturers continuously from January 2009 to January 2012. These larger payment amounts are one key reason why the subsidy program tends to enhance profitability more than the risk reduction programs.

Table 4. Model-Predicted Cumulative Program Costs for Various Scenarios, by Product, $ million

Program MPC42 MPC70 MPC90 Casein CaseinatesNo program 0.0 0.0 0.0 0.0 0.0Unit Subsidy 0.3 0.3 0.1 0.4 0.2Risk 0.0 0.7 0.9 0.3 0.0Capacity Risk 0.1 5.3 113.8 0.3 0.0Capacity Subsidy 6.0 6.8 4.1 5.5 5.2

Capacity Subsidy less Capacity Risk 5.9 1.6 -109.7 5.2 5.1

Figure 36. Model-Predicted Impacts of Programs on Unit Profitability of MPC42

– 44 –

Figure 37. Model-Predicted Impacts of Programs on Unit Profitability of MPC70

Figure 38. Model-Predicted Impacts of Programs on Unit Profitability of MPC90

– 45 –

Figure 39. Model-Predicted Impacts of Programs on Unit Profitability of Casein

Figure 40. Model-Predicted Impacts of Programs on Unit Profitability of Caseinate

– 46 –

Figure 41. Model-Predicted Cumulative Program Costs for MPC42

Figure 42. Model-Predicted Cumulative Program Costs for MPC70

– 47 –

Figure 43. Model-Predicted Cumulative Program Costs for MPC90

Figure 44. Model-Predicted Cumulative Program Costs for Casein

– 48 –

Figure 45. Model-Predicted Cumulative Program Costs for Caseinate

Results: US Market Impacts of Investments in Protein Product Manufacturing Capacity and Investment Incentive Programs under 2006 Market Conditions

The dynamic dairy sector model can also be used to assess the market impacts of various factors, particularly the expansion of capacity with and without investment incentive programs. Assuming 2006 market conditions as a baseline, we examine the impacts on producer milk prices, producer revenues, protein product prices, manufacturer net margins, program costs and net gains from implementing the incentive programs (Table 5). In the absence of capacity expansion, a subsidy program would have limited impacts on protein product production or prices, but would increase unit net margins and cumulative earnings by US protein product manufacturers at a cost of about $2.7 million (Table 5).

Under the assumption of expanded capacity, our results suggest that increases in US production of protein products would (under initial 2006 conditions) increase the average producer all-milk price by about $0.09/cwt and cumulative discounted producer revenues by about $1.5 billion through January 2012 (six years after capacity is added). US protein product prices would be lower due to increased production, and US protein product manufacturers would see reductions in unit net margins (Table 5). However, cumulative net margins for US dairy protein product manufacturers would be significantly larger, except for MPC90. Under a risk reduction program when capacity is expanded, the producer all-milk price increases by $0.12/cwt compared to the 2006

– 49 –

baseline, and cumulative discounted producer revenues increase by $2.0 billion. A subsidy program would increase the all-milk price by $0.10/cwt and cumulative discounted producer revenues by $1.7 billion. The risk reduction and subsidy programs increase both unit and cumulative net margins for protein product manufacturers. Manufacturer net margins would be predicted to increase by $155 million and $122 million under the two programs, respectively. The cumulative program cost of the risk reduction program would total nearly $143 million over 6 years (but only about $38.7 million) if MPC90 was excluded. The subsidy program would have a substantially lower cumulative cost (albeit for only three years) of $48 million (or $39 million if MPC90 is excluded).

For each of the scenarios in which an incentive program is implemented, the ratio of overall monetary gains by dairy producers and protein product manufacturers is larger than the amount expended on the program (Table 5). The ratio of gains to program cost is largest for the subsidy program with capacity expansion (nearly 4 times greater benefits than costs), although the overall gains are significantly larger for the risk reduction program, especially if it includes MPC90.

It is important to note, however, that the dynamic effects of expanding capacity and operating incentives programs change over time (Figures 46 through 52). For example, the impact on the all-milk price is largest during the first few months after capacity comes online, reaching a peak between $0.60/cwt and $0.80/cwt higher than the initial value, depending on the incentive program (Figure 46). After that time, the all-milk price declines more or less continuously as milk supplies respond to the higher prices. By January 2012, all-milk prices are about $0.20/cwt lower than in 2006, even with the expanded capacity. A similar but somewhat more exacerbated pattern exists for the Class IV price (Figure 47). In fact, given relatively low 2006 NDM prices as a baseline, the Dairy Price Support Program (DPSP) would begin to make modest purchases of NDM starting in 2010 even with the additional capacity in response to additional milk supplies. The DPSP would also begin butter purchases in response to the reduction in the butter price that accompanies increased demand for skim solids and increased production of butterfat in raw milk (in response to higher producer prices). Note that this is not to say that government costs will be increased under current market conditions. Rather, the lesson here is that the types of effects that would be observed in other dairy product markets in response to expansion of capacity and incentive programs bear consideration.

Similarly, the profitability of protein product manufacture will vary over time in response to expanded US capacity (Figures 48 through 52), as was noted previously for the analyses based on 2006 to 2008 actual milk prices. In general, a subsidy program in the absence of capacity expansion will provide sustained increases in unit margins for protein products, because the US market share is small enough that resulting production increases due to the subsidy to not markedly decrease prices. Profitability is negatively affected in the short run by the addition of capacity and associated lower prices. A subsidy program under capacity expansion will increase protein product manufacturer unit margins compared to capacity expansion without the subsidy. A risk

– 50 –

reduction program under capacity expansion typically enhances unit margins in the short run compared to a subsidy program, except for caseinate (for which risk reduction payments are relatively small and are predicted to end by early 2008). In the longer term, however, unit margins end up higher with the subsidy program, except for MPC90 (for which the risk reduction program would be highly active throughout the simulated time frame).

Table 5. Results of Scenarios Based on 2006 Market Conditions

Scenario

Baseline Subsidy Capacity Capacity Risk Capacity Subsidy

Impact of program 2006 Conditions

$0.10/lb subsidy on production

Introduce 1 plant of each type at t=12

Capacity, with program to guarantee

returns equal to NDM

Capacity, with $0.10/lb

subsidy on production

All-Milk Price, $/cwt 13.01 13.01 13.10 13.13 13.11Class IV Price, $/cwt 11.06 11.06 10.94 10.94 10.95Cumulative Discounted Producer Revenues, $ bil 123.5 123.5 125.0 125.5 125.2

Difference from Baseline, $ mil 0.3 1538.4 1999.4 1681.4Difference due to program, $ mil 0.3 461.0 143.0

US Protein Product Price, $/cwtMPC42 139.0 139.0 123.7 122.8 121.1MPC70 207.8 208.0 203.4 202.4 202.8MPC90 239.0 239.2 212.1 189.7 210.4Casein 301.5 301.5 299.3 298.5 299.0Caseinate 341.5 341.6 332.4 332.6 331.9

Protein Product Net Margin, $/cwtMPC42 40.5 48.8 20.3 24.5 25.9MPC70 23.3 31.9 10.8 22.5 18.1MPC90 -39.3 -30.7 -73.8 -21.0 -67.8Casein 68.8 77.2 53.2 61.5 60.5Caseinate 113.9 122.3 90.4 91.8 97.6

– 51 –

Table 5. Continued...

Scenario

Baseline Subsidy Capacity Capacity Risk Capacity Subsidy

Impact of program 2006 Conditions

$0.10/lb subsidy on production

Introduce 1 plant of each type at t=12

Capacity, with program to guarantee

returns equal to NDM

Capacity, with $0.10/lb

subsidy on production

Cumulative Protein Processor Net Margin, $ mil

MPC42 2.9 3.6 17.1 22.7 26.0MPC70 1.9 2.6 12.1 30.4 23.4MPC90 -0.9 -0.7 -61.9 -24.1 -58.4Casein 7.0 7.8 44.1 54.9 53.1Caseinate 6.7 7.2 68.8 70.9 77.8Total 17.6 20.7 80.1 154.7 121.9Difference due to program 3.0 74.6 41.8

Total without MPC90 18.5 21.3 142.0 178.8 180.4Difference due to program 2.8 36.8 38.3

Cumulative Program Cost, $ milMPC42 0.0 0.6 0.0 6.5 10.8MPC70 0.0 0.7 0.0 20.5 12.1MPC90 0.0 0.2 0.0 104.0 8.3Casein 0.0 0.8 0.0 9.8 8.4Caseinate 0.0 0.5 0.0 1.8 7.9Total 0.0 2.8 142.6 47.5

Total without MPC90 0.0 2.7 0.0 38.7 39.2

Program Returns IndicatorsGains to Dairy Producers, $ mil 0.3 461.0 143.0Gains to PP Manufacturers, $ mil 3.0 74.6 41.8Costs of Program, $ mil 2.8 142.6 47.5Net Gains, $ mil 0.5 393.0 137.4Ratio of Gains to Costs 1.2 3.8 3.9

– 52 –

Figure 46. Model-Predicted Impact of Various Scenarios on Producer All-Milk Price

Figure 47. Model-Predicted Impact of Various Scenarios on Class IV Milk Price

– 53 –

Figure 48. Model-Predicted Impact of Various Scenarios on MPC42 Profitability

Figure 49. Model-Predicted Impact of Various Scenarios on MPC70 Profitability

– 54 –

Figure 50. Model-Predicted Impact of Various Scenarios on MPC90 Profitability

Figure 51. Model-Predicted Impact of Various Scenarios on Casein Profitability

– 55 –

Figure 52. Model-Predicted Impact of Various Scenarios on Caseinate Profitability

Summary

The United States has not produced a significant volume of casein or caseinate for many years. MPCs are relatively new dairy ingredients and, like casein products, the majority of domestic use has been supplied by lower cost imports. However, modeling suggests that the combined effects of a weak U.S. dollar and weaker than expected world milk production (due to policy changes in Europe and drought in Oceania) have altered the competitive landscape.

Lower milk protein concentrate products (MPC 42), casein and caseinates appear to have the greatest likelihood of profitable U.S. production. Profitability of these products, even under sensitivity analyses which include a rebound in Oceania milk production appears likely. Various scenarios for dairy farmer funded support of protein product production have positive cost-benefit ratios although some forms of subsidy could become fairly costly.

Dairy farmer revenues are enhanced by domestic production of milk protein products. However, dynamic modeling suggests that producers will respond to higher milk prices with more milk production eventually eroding much of the initial price gains.

– 56 –

References

Bishop, P. M., and C. F. Nicholson. 2004. Dairy Market Impacts of US Milk Proteins and Policy Alternatives. Department of Applied Economics and Management, Cornell University. [Research Bulletin 2004-08]Cornell Program on Dairy Markets and Policy. 2006. Dairy Contacts Listing. http://www.cpdmp.cornell.edu/CPDMP/Pages/Data/ Jesse, Edward V. and William D. Dobson. 2007. U.S. Dairy Trade: Situation and Outlook, 2007. Babcock Institute Discussion Paper No. 2007-1.Nicholson, Charles F. and Thomas Fiddaman. 2003. Dairy Policy and the Origins of Dairy Price Volatility. Paper presented at the 21st International Conference of the System Dynamics Society, July 20-24, 2003, New York, NY.Nicholson, C. F. and H. M. Kaiser. 2008. Dynamic Impacts of Generic Dairy Advertising. Journal of Business Research (in press). Nicholson, C. F. and M. W. Stephenson. 2006. Market Impacts of High-Protein Whey Product Promotion. Report to Dairy Management, Inc., January.Schmit, T. M. and H. M. Kaiser. 2006. Forecasting Fluid Milk and Cheese Demands for the Next Decade. Journal of Dairy Science, 89:4924-4936.Sterman, John D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill.Stephenson, Mark W. 2006. Cost of Processing in Cheese, Whey, Butter and Nonfat Dry Milk Plants. http://www.dairy.cornell.edu/CPDMP/Pages/Publications/Pubs/COP%20Working%20Paper.pdfTetra Pak Processing Systems. 2003. Dairy Processing Handbook, 2nd edition. Lund, Sweden: Tetra Pak Processing Systems AB.US International Trade Commission. 2004. Conditions of Competition for Milk Protein Products in the U.S. Market. Investigation No. 332-453, May. [USITC Publication 3692]Ventana Systems, Inc. 2005. Vensimʼs Userʼs Guide, Version 5.

– 57 –