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The Effects of Ethanol on Texas Food and Feed

AFPC

Agricultural and Food Policy Center

Texas A&M University

David P. Anderson, Joe L. Outlaw, Henry L. Bryant, James W. Richardson, David P. Ernstes, J. Marc Raulston, 

J. Mark Welch, George M. Knapek, Brian K. Herbst, and Marc S. Allison

April 10, 2008
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Table of Contents

1. Executive Summary.................................................................................................................................. 3

2. Ethanol and By-Products Overview......................................................................................................... 4

3. Texas Crop Production Costs................................................................................................................. 10

4. Livestock Production Costs.................................................................................................................... 16

5. Impact on Retail Prices – Farm Share of Retail Prices........................................................................... 20

6. Effects on Equilibrium Food Market Prices and Livestock Margins....................................................... 23

7. Supply and Demand: Some Reasons for High Prices............................................................................. 29

8. Futures and Funds .................................................................................................................................. 31

9. Estimating the Variability in Corn Price ................................................................................................ 33

10. Economic Impacts of Higher Corn Prices on the Texas Economy......................................................... 34

11. Effects of a Renewable Fuel Standard Waiver........................................................................................ 35

12. References............................................................................................................................................... 39

AFPC

© 2008 by the Agricultural and Food Policy Center

Research Report 08-1

Agricultural and Food Policy Center

Department of Agricultural Economics

2124 TAMU

College Station, TX 77843-2124

Web site: www.afpc.tamu.edu
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1 Executive 

Summary

There are many related and difficult 

questions raised by the fuel vs. food vs. 

feed debate. Some of those questions may 

be irrelevant or of less importance when 

compared to the real issues to be addressed. 

Clearly, there are winners and losers in 

Texas and U.S. agriculture. This report addresses a series of the common questions 

raised in the debate.

The key findings contained in this report are:

•	 The underlying force driving changes

in the agricultural industry, along with 

the economy as a whole, is overall 

higher energy costs, evidenced by $100 

per barrel oil.

•	 With rising energy costs, corn and

other commodity prices would have to 

increase. Rising fertilizer costs led to 

a 3 million acre reduction in planted 

corn acres in the 2006-07 crop year. 

Higher production costs will continue 

to pressure acres.

•	 This research supports the hypothesis

that corn prices have had little to do 

with rising food costs. Higher corn 

prices do have a small effect on some 

food items.

•	 Important food items like bread, eggs,

and milk have high prices that are 

largely unrelated to ethanol or corn 

prices, but correspond to fundamental 

supply/demand relationships in the 

world.

•	 Speculative fund activities in futures

markets have led to more money in the 

markets and more volatility. Increased 

price volatility has encouraged wider 

trading limits. The end result has been 

the loss of the ability to use futures 

markets for price risk management 

due to the inability to finance margin 

requirements.

•	 The potential exists for even higher

corn prices based on historical yield 

variability. Fewer corn acres planted in 

2008 leave production susceptible to 

weather risks. Small yield reductions 

will result in even higher prices.

•	 The livestock industry has borne the

costs of higher corn prices. The structure of the industry has made it unable 

to pass costs on, either up or down the 

supply chain.

•	 The livestock industry is in the middle

of this transition, and prices don’t yet 

reflect the impact of higher costs.

•	 The net balance to the Texas agricultural economy is negative. While corn 

and grain sorghum producers benefit 

from high prices, the livestock industry faces increasing costs. Because the 

livestock industry is bigger than the 

crop industry, the net balance is negative.

•	 Relaxing the RFS does not result in

significantly lower corn prices. This is 

due to the ethanol infrastructure already in place and the generally positive economics for the industry. The 

ethanol industry has grown in excess 

of the RFS, indicating that relaxing the 

standard would not cause a contraction 

in the industry.
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The boom in corn-based ethanol production in the United States has led to 

sharply higher corn prices and, by extension, higher soybean and other crop prices 

as farmers have shifted acres between crops. 

High prices for some crops like wheat have 

other causes. The ethanol, or biofuel, revolution has, in turn, been caused by rapidly 

increasing oil prices, aided by government 

policies and the desire for cleaner burning 

fuels to ease global warming fears. 

The overall effect on agriculture and 

the economy, as a whole, is complex. While 

corn prices have increased, they are offset 

by higher fertilizer and fuel prices. Higher 

feed costs have caused large increases in 

production costs for livestock producers. 

Rising production costs are being felt by 

producers and consumers throughout the 

economy.

This study was prompted by a number of questions asked by livestock and 

crop producers, elected officials, media, and 

consumers about the reasons for cost and 

price changes and what the impact of these 

changes will be on the agricultural and 

broader economy. This report is developed 

to address those questions and is organized 

as follows:

•	 An overview of the basics of corn 

based ethanol, by-product feeds from 

the ethanol industry, and the energy 

market. 

•	 The impact of rising costs on crop and 

livestock production costs. 

•	 The impact of higher corn and energy 

prices on retail food prices.

•	 Reasons for higher agricultural commodity prices, including the role of 

speculative funds in futures markets 

and the supply and demand situation 

for various commodities.

•	 Potential for higher corn prices given 

weather problems.

•	 Summary of costs to the livestock industry and benefits to the grain industry.

•	 Potential impacts of relaxation of the 

renewable fuel standard on corn prices.

Economic Overview

No discussion or research on any of 

these issues would be complete without an 

overview of the overall energy complex. Oil 

prices have increased over the last 4 years 

from $35 per barrel in 2005 to over $100 

per barrel in early 2008 (DOE-EIA). The 

impact of sharply higher oil prices continues to ripple through the economy as 

businesses and consumers deal with higher 

prices. Oil prices alone are an incomplete 

look at energy markets and the role of 

higher prices in the economy. Energy prices from all sources have increased over the 

same time period. Natural gas prices have 

increased largely due to the increased demand for it in the production of electricity. 

Natural gas is also the major input in producing nitrogen fertilizer. 

Oil prices reflect not only increased demand in the growing economies of the developing world, including China and India, 

but political instability in major producing 

countries, refining infrastructure, shipping, 

and, for the United States, the effect of the 

weaker dollar on the cost of imported oil. 

Ethanol Industry Overview

Global interest in biofuels production 

and consumption has surged over the past 5 

years. While Brazil (ethanol) and Germany 

2 Ethanol 

and ByProducts 

Overview

Table 2.1: Ethanol Production for 

All Uses for Selected Countries, 

2004-2006.

2004 2005 2006

Million Gallons

Brazil 3,989 4,227 4,491

U.S. 3,535 4.264 4,855

China 964 1,004 1,017

India 462 449 502

France 219 240 251

Russia 198 198 171

South Africa 110 103 102

U.K. 106 92 74

Saudi Arabia 79 32 52

Spain 79 93 122

Thailand 74 79 93

Germany 71 114 202

Ukraine 66 65 71

Canada 61 61 153

Poland 53 58 66

Indonesia 44 45 45

Argentina 42 44 45

Italy 40 40 43

Australia 33 33 39

Japan 31 30 30

Pakistan 26 24 24

Sweden 26 29 30

Philippines 22 22 22

South Korea 22 17 16

Guatemala 17 17 21

Cuba 16 12 12

Ecuador 12 14 12

Mexico 9 12 13

Others 364 732 297

Total 10,770 12,150 13,489

Source: Renewable Fuels Association (http://www.ethanolrfa.

org).

(biodiesel) have relatively more mature biofuels markets, countries such as the United 
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States, Canada, China, and India have recently elevated bioenergy production and 

consumption in terms of national importance. For example, in the United States, 

ethanol has gone from initially drawing 

support from a small number of commodity groups and some environmentalists to 

being counted on to:

•	 help lessen reliance on foreign oil imports, 

•	 increase farm commodity prices thereby reducing commodity program expenditures,

•	 enhance the perception of being more

environmentally conscious with fuels 

that are generally considered more environmentally friendly, and 

•	 enhance rural development through a

dispersed ethanol industry.

Table 2.1 contains the 2004 to 2006 

annual ethanol production (for all uses not 

necessarily transportation fuel) of the major producers in the world. Brazil and the 

United States are by far the largest producers in the world. The U.S. ethanol industry initially began to take shape in the 

late 1970s, producing what was then called 

“gasohol” in response to a doubling of oil 

prices (nearly $30 per barrel). As a result 

of crude oil prices rising to nearly $40 per 

barrel in the early 1980s, the industry expanded rapidly and by the middle 1980s, 

an estimated 170 plants were producing 

approximately 400 million gallons per year 

(Vander Griend). However, by July 1986, 

the price of oil retreated back to $10 per 

barrel and the gasohol industry collapsed 

as costs per gallon were not competitive 

with gasoline at lower oil prices. Few stayed 

in the industry, but those that did began 

focusing on decreasing production costs. 

By the late 1990s, the costs of production 

(primarily due to larger plants realizing 

scale economies, reduced enzyme costs, 

and higher corn to ethanol conversions) 

for ethanol were competitive with gasoline. It should be noted that the blenders 

tax credit remained in place throughout the 

period described here providing incentive 

to blenders for using ethanol. 

There are 147 ethanol plants in operation in the United States with around 55 

more under construction (Renewable Fuels 

Association). Over the past few years, the 

U.S. ethanol industry has been expanding 

as fast as plants could feasibly be built. Currently, as corn prices have increased, some of 

the proposed ethanol plants have dropped 

their plans and/or put them on hold. Most 

industry observers realize the Renewable 

Fuels Standard (RFS) contained in the Energy Policy Act of 2005 was never binding. 

However, this may not be the case with the 

RFS of 15 billion gallons of grain based 

ethanol mandated in the Energy Independence and Security Act of 2007. The higher 

mandate will likely encourage the build out 

of ethanol capacity to at least the 15 billion 

gallon per year level.

Governments around the world have 

enacted policies designed to encourage 

biofuels production, encourage biofuels use, 

and protect biofuel producers from international competition. Some countries, such 

as the United States, have policies in place 

to do all three for the ethanol industry. The 

U.S. has encouraged ethanol use by providing a motor fuel tax exemption and/or credit 

for more than 20 years. The U.S. also has a 

secondary tariff on imported ethanol from 

outside the Caribbean Basin which serves 

to protect the ethanol industry from imports. In the author’s opinion, the two most 

significant policy/regulatory changes that 

have resulted in the growth of the ethanol 

industry are: the decision not to provide oil 

companies protection from litigation for 

using MTBE as an oxygenate (MTBE had 

been found to be a carcinogen in drinking 

water in California) and the enactment of 

the 2005 energy bill (discussed later) that 

required a specific amount of renewable fuels (ethanol and biodiesel) be blended into 

fuel supplies each year from 2006 through 

2012. 

In the short-run, it can be argued that 

some encouragement is needed to develop 

a new industry through government policies as well as policies that are designed to 

protect a new industry from international 

competition. However, in the long run, the 

cost of production will determine whether or not biofuels can be viewed as viable 

energy alternatives. Ethanol production is 

generally perceived in a positive light by the 

public. However, many industry observers 

wonder whether the industry will crumble 

when, and if, the price of oil declines or 

the government reduces or eliminates the 

blender’s tax credits. The answer is – it depends. The price of oil only gives you part 

of the information needed to address this 

question, the other part being the ethanol 

costs of production. The most important 

factor in the cost of production is feedstock 

costs, because they make up over twothirds of production costs. While it doesn’t 

seem likely today, last year at this time the 

price of corn in the U.S. was slightly more 

than one-half where it is today – over $5 

per bushel. With or without government 

support, there will likely be combinations 

of low and high oil prices and feedstock 

costs that result in profits or losses for the 

ethanol sector. 

Table 2.2 illustrates that the net income 

for a typical ethanol plant varies substanTable 2.2: Net Returns for a Typical 100 Million Gallon per Year Ethanol Plant at Various Ethanol and Corn 

Prices.

Corn Price ($/bu, FOB)

Ethanol Price 

($/gal)

3.00 3.50 4.00 4.50 5.00 5.50 6.00

1.50 (0.17) (0.30) (0.43) (0.55) (0.68) (0.81) (0.94)

1.75 0.08 (0.05) (0.18) (0.30) (0.43) (0.56) (0.69)

2.00 0.33 0.20 0.07 (0.05) (0.18) (0.31) (0.44)

2.25 0.58 0.45 0.32 0.20 0.07 (0.06) (0.19)

2.50 0.83 0.70 0.57 0.45 0.32 0.19 0.06

2.75 1.08 0.95 0.82 0.70 0.57 0.44 0.31 

3.00 1.33 1.20 1.07 0.95 0.82 0.69 0.56 
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tially depending on feedstock (corn) costs 

and the ethanol price. Currently, the corn 

price in the U.S. is near $5.50 per bushel, 

and the ethanol price is just over $2.50 per 

gallon. With this combination, a typical 

ethanol plant would be expected to realize $0.19 per gallon in net income. While 

positive, these profit levels are not as likely 

to spur additional investment. Clearly, this 

is the reason why some proposed ethanol 

plants have put their plans on hold. However, for those plants already built, it is in 

their best interest to continue to produce as 

long as they can cover their variable costs 

increasing the amount a plant could pay 

for corn roughly $0.10 per bushel over the 

break-even level – roughly $6.15 per bushel 

of corn at $2.50 per gallon of ethanol. As 

indicated in Table 2.2, a large number of 

ethanol and corn price combinations result 

in negative net cash income for the typical plant. While these numbers are typical 

of a 100 million gallon per year plant, each 

potential plant location has attributes or 

drawbacks that could tilt (both positively 

and negatively) the economic picture for 

that plant location. For example, ethanol 

plants in Texas are expected to reap benefits 

from their proximity to cattle feeding that 

offset, to a degree, the drawback of being in 

a corn deficit state.

By-products of Ethanol Production

The major by-product feeds from current corn based ethanol production are 

corn gluten feed from wet-mill ethanol 

plants and distiller’s grains from dry grind 

ethanol plants. Distiller’s grains may be wet 

or dry and may be combined with solubles 

to yield the more commonly discussed distiller’s grains with solubles. As most of the 

increase in ethanol production is from the 

dry grind process, the remainder of the discussion will focus on distiller’s grains.

The dry grind ethanol process yields 

about 2.75 gallons of ethanol and 17-18 

pounds of distiller’s grains per bushel of 

corn. The removal of starch for ethanol 

concentrates the remaining nutrients in the 

distiller’s grains. The distiller’s grains contain a higher level of protein, energy (from 

the fat), phosphorus, and sulfur than are 

found in corn grain.

Disadvantages

Several disadvantages can be associated with distiller’s grains:

•	 It is a highly variable product that may 

require testing for nutritional content 

to maintain ration balance. The product varies by batch and by plant. Efforts 

are underway to create a national standard. Private companies are marketing 

their own distiller’s grains and, in some 

cases, are trying to keep a standard for 

those products.

•	 It is costly to dry given natural gas 

prices, but in its wet form it is costly to 

ship due to the water content. 

•	 Flowability, the ability of the product 

to flow out of the shipping container, 

has been a problem when shipping 

distiller’s grains in rail cars and trucks 

long distances due to particle size and 

the product compacting during travel.

•	 Wet distiller’s spoils in a short period 

(3-5 days) and so must be fed quickly. 

When feeding wet distiller’s grains, 

very little inventory is kept on hand as 

the product is fed almost as delivered. 

Interruptions in delivery are critical in 

this situation, whether the interruption 

is due to the plant production schedule 

or weather problems.

•	 Phosphorus and/or sulfur content can 

be a problem when feeding distiller’s 

grains for confined livestock feeding 

and for supplemental feeding to beef 

cows.

•	 The ethanol production process does 

not destroy mycotoxins, like aflatoxin. 

The production process actually results 

in a 3x concentration of aflatoxin in the 

distiller’s grains, relative to the amount 

in the corn.

Advantages

There are some advantages to distiller’s 

grains, as well:

•	 Distiller’s grains provide an additional

feed for livestock producers to help 

offset higher corn prices and reduced 

availability as corn is sent to ethanol 

plants.

•	 Research indicatesthat distiller’s grains

typically have higher protein and energy content than corn. 

•	 Research indicates that inclusion of

distiller’s grains in feedlot cattle rations can lead to increased gain per day 

and reduced costs of gain.

There are limits with how much distiller’s grains can be fed to different species. Research indicates that they can make 

up 35-40 percent (dry matter) of feedlot 

cattle rations. Dairy cow rations can contain 10-20 percent distiller’s grains. Hogs, 

broilers, and turkey rations may contain 

up to 10 percent. The limiting factor varies 

by species, but often includes the type and 

source of the fat in distiller’s grains and its 

interactions with meat quality, fat characteristics, and milk components. Regardless 
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0

0.5

1

1.5

2

2.5

Jan-82Jan-85Jan-88Jan-91Jan-94Jan-97Jan-00Jan-03Jan-06

DDG/Corn

Figure 2.1: Corn and Dried Distiller’s Grain (DDG) Price Ratio.

of the issues associated with feeding distiller’s grains, these by-products have been fed 

successfully by many livestock producers 

for years. Further research is underway to 

examine ways to successfully include more 

distiller’s grains in rations.

Distiller’s Grains Prices

By-products of ethanol production are 

not new to the feed market. What is new 

is their perceived importance as an alternative feedstuff in this environment of historically high corn prices. The conventional 

wisdom has been that the impact of high 

corn prices on costs of production in livestock operations could be largely offset by 

the availability of relatively inexpensive byproducts – primarily DDG and/or DDGS 

(dry distiller’s grains with solubles). The behavior of DDG prices in relation to corn 

prices is a simple empirical question. 

With respect to the level of DDG 

prices in comparison to corn prices, Figure 2.1 plots DDG price as a percentage 

of corn price (with both prices converted 

to $/lb, as fed) January 1982 through October 2007. DDG prices are wholesale prices 

for Lawrenceburg, Illinois, and corn prices 

are Texas Triangle prices received by farmers for corn – both reported by USDAAMS. Clearly, over time – or at least since 

about mid-1985 – the price of DDG as a 

percentage of the corn price for the same 

period has trended lower. This supports the 

notion that by-products have become relatively cheaper with increased availability.

Another aspect of the relationship 

between corn and DDG prices is the responsiveness of DDG prices to corn price 

changes. If DDG are a good substitute 

for corn, one would expect their prices to 

be closely correlated. Figure 2.2 is a scat0

1

2

3

4

5

6

40 90 140 190 240

DDG ($/ton)

Corn ($/bu)

Figure 2.2: Corn and Dried Distiller’s Grain (DDG) Price, 1982-2007.

Table 2.3: Correlation between 

Corn and Distillers’ Dried Grain 

(DDG) Prices: 1982-2007.

Time Period Correlation Coefficient

1982-2007 0.510

1982-1986 0.483

1987-1991 0.480

1992-1996 0.710

1997-2001 0.794

2002-2006 0.602

2005-2007 0.849
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ter diagram of corn and DDG prices from 

January 1982 through October 2007. These 

are the same price series as discussed in the 

previous figure. As the simple linear equation shows, there is a generally positive relationship between corn and DDG prices; 

however, the association between the two 

series over the entire time period presented 

here does not appear to be all that strong. 

The correlation coefficient between the two 

series is only about 0.44. 

Further investigation of the relationship between corn and DDG prices suggests that the relationship between the two 

price series has not been all that consistent 

over time. Table 2.3 shows correlation coefficients for each 5-year period from 1982 

through 2006 as well as for the 2005-2007 

time period. This data indicates, in general, a closer relationship between corn and 

DDG prices in about the latter half of the 

data, with a very close relationship over the 

last two or three years (Anderson, Anderson, and Sawyer). 

The evaluation of DDG and corn prices presented here suggests that DDG prices have become somewhat less expensive 

relative to corn over time. However, there 

is some evidence to suggest that DDG and 

corn prices are more closely related now 

than in earlier years of DDG production. 

The significance of this information for 

livestock producers is twofold. First, DDGs 

may be an inexpensive feed in a relative 

sense; however, they will not likely be an 

inexpensive feed in any absolute sense. 

Second, DDG prices may become more 

volatile, with DDG prices more closely following the movement of corn price as more 

and more producers enter the DDG feed 

market.

The foregoing evaluation of DDG and 

corn prices reflects on the national market 

for corn and DDG. Clearly, some producers are more advantageously situated than 

others with respect to using DDG as a feed 

source. Producers who are able to source 

wet distiller’s grain, for example, may in 

fact find access to a feed that is inexpensive 

not only relative to corn, but in absolute 

terms as well. Of course, transporting and 

handling this type of feed involves special 

considerations and will only be an option 

for producers situated very close to a source 

of supply. A second and related caveat to 

the preceding analysis is that using DDG 

wholesale prices reported by USDA masks 

the significant transportation costs that 

most producers will incur in obtaining 

DDG. Corn is widely produced around the 

country, and a well-developed infrastructure for storing and moving corn efficiently 

around the country currently exists. The 

same is not true for DDG. DDG production is still largely concentrated in the Corn 

Belt. Getting DDG to other parts of the 

country involves considerable transportation expense that, for producers in many 

parts of the country, will quickly erode any 

relative price advantage of DDG compared 

with corn.

Impact of By-Product Feeding on Livestock Costs of Production

As noted earlier, the availability of 

by-products from ethanol production has 

been viewed as an important resource for 

helping livestock producers deal with the 

increased competition for grain. Considerable work has been done on the feasibility 

of feeding the by-products of distillation, 

and some of this work pre-dates the current 

surge in ethanol production. For example, 

Larson et al. were exploring the feeding 

value of distillery by-products in the early 

1990s. Of course, recently, interest in byproduct feeds has intensified greatly. This 

has spurred considerable research into the 

technical aspects of effectively using these 

feed sources. (For a fairly current review of 

this work, see Cole et al.) 

Economic evaluation of by-product 

feeding systems remain, for the most part, 

very preliminary. Anderson, Daley, and 

Outlaw develop budgets to compare cattle 

feeding returns with and without the inclusion of by-products (wet and dry distiller’s 

grains). In their study, they find that including WDGS (wet distiller’s grains with 

solubles) in a ration with dry rolled corn 

results in the lowest cost of gain. Interestingly, WDGS fed in conjunction with 

steam flaked corn results in the highest cost 

of gain of the alternatives considered. They 

note that their results depend rather critically on assumptions related to feed conversion and average daily gain for each of 

the rations considered.

To provide further insight into the 

effect of by-product feeding on producer 

returns, we simulated feeding returns for a 

Texas and Nebraska feedlot using DDGS 

(Texas) and WDGS (Nebraska) in their rations. Rations and associated average feed 

conversion rates were taken from Anderson, Daley, and Outlaw. Prices for ration 

components were simulated from a lognormal distribution of prices using parameters (mean and standard deviation) calculated using price data from 2000 to 2007. 

For each year, May through September average prices were used to be consistent with 

a Spring placement/Fall slaughter feeding 

scenario. All prices were correlated using a 

procedure described by Naylor et al. (See 
9

Anderson and Zeuli for a similar application of this procedure.)

Feed conversion rates were simulated from a triangular distribution with 

the mode taken to be the feed conversion rate associated with each ration in 

Anderson, Daley, and Outlaw. Minimum 

and maximum feed conversion rates were 

taken from Kansas State University feedlot 

closeout data (Livestock Marketing Information Center). Minimum and maximum 

feed conversion rates from the past ten 

years of August through October monthly 

closeouts were calculated as a percentage of 

the mean. These percentages were applied 

to the mode used for each ration to define 

minimum and maximum values for simulation from the triangular distribution.

Results of this simulation are presented in Table 2.4. The most significant feature 

of these results is that the availability of wet 

distiller’s grains in Nebraska appears to 

convey a considerable competitive advantage. This is evidenced by the rather significant improvement in certainty equivalents 

in moving from the base ration to the 30 

percent WDGS ration. WDGS could be 

fed in Texas, of course; and a preliminary 

calculation of the effect of a 30 percent 

WDGS ration on the profitability of the 

Texas feedlot did show a positive impact 

on profitability. However, as noted above, 

WDGS appears to not fit well into rations 

with steam flaked corn. This is the primary concentrate feed in Texas feedlots, and 

considerable fixed investment is in place 

to accommodate steam flaking. Thus, for 

Texas feedlots, transitioning to WDGS is 

probably a longer-term proposition than it 

is in some other regions. The ability to feed 

DDGS does confer some benefit in terms 

of profitability, but this benefit appears – at 

this point – to be marginal in comparison 

to that which can be obtained from introducing WDGS to a dry rolled corn feeding 

system.

DDGS Supply and Demand

The supply of distiller’s grains is directly related to the amount of ethanol 

produced because the yield is about 17 

pounds per bushel of corn distilled. Current ethanol production projections indicate supplies growing to over 35 million 

tons by 2012. In the future, the demand for 

distiller’s grains will depend almost entirely 

on whether there are any cost advantages 

associated with distiller’s grains relative to 

other feedstocks – primarily corn.

Table 2.4: Comparison of Cattle Finishing Returns in Texas and Nebraska Feedlots using By-Product Feeds.

Return Over Variable Costs (Dollars per Head)

Base Ration 15% DDGS 15% WDGS 30% WDGS

Texas

Average $87.06 $87.74 10.50 12.96

Std. Dev. $17.89 $17.30 33.50 48.58

Certainty Equivalent $84.62 $87.14 0.00 0.00

Nebraska

Average $41.10 $65.91 $83.26

Std. Dev. $34.41 $22.62 $17.15

Certainty Equivalent $38.86 $64.88 $82.67

Notes: Base ration for Texas includes steam flaked corn as the primary energy feed. Base ration for Nebraska includes dry rolled corn as the primary 

energy feed. Certainty equivalents are reported for a constant relative risk aversion coefficient of 2 (moderately risk averse) (Hardaker Huirne, and 

Anderson).
10

3 Texas Crop 

Production 

Costs

Cash receipts have increased over the 

last two years for the majority of crop producers across the United States; however, 

the often overlooked reality is farmers are 

facing unprecedented increases in input 

prices, leading to declining margins for 

many. This section will discuss major production cost increases currently faced by 

producers. Using a crop budget framework, 

specific expected cost increases will be examined for major crops in key production 

regions of Texas. 

In addition to more heavily publicized 

increases in fuel and fertilizer prices, today’s producers are facing increases in labor 

costs and replacement costs for machinery. 

Steel and other raw material prices are on 

the rise, leading to heftier repair and maintenance bills. Advances in plant breeding 

and pesticides have enhanced productivity; however, more expensive seed and 

chemicals have offset much of the potential 

profitability of higher yields. Figure 3.1 is 

a graph of monthly indices of prices paid 

by farmers for inputs over the last decade, 

reflecting the upward trend in costs faced 

by producers every day.

Although costs of production have 

risen across the board, the impacts of increasing fuel and fertilizer prices are the 

most prevalent. Since February 2006, the 

average U.S. retail diesel price has climbed 

from $2.48/gallon to $3.38/gallon in February 2008, a 36.5 percent increase (Figure 

3.2). Fuel price increases impact producers 

directly as they run tillage equipment, harvest and haul crops, and operate irrigation 

equipment. Indirect effects occur as custom 

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Percent of Base Years (1992-1994)

Seeds

Mixed Fertilizer

Insecticides

Herbicides

Fuels

Wage Rates

Repairs

Interest

Jan-1998

Mar-1998

May-1998

Jul-1998

Sep-1998Nov-1998

Jan-1999

Mar-1999

May-1999

Jul-1999

Sep-1999Nov-1999

Jan-2000

Mar-2000

May-2000

Jul-2000

Sep-2000Nov-2000

Jan-2001

Mar-2001

May-2001

Jul-2001

Sep-2001

Nov-2001

Jan-2002

Mar-2002

May-2002

Jul-2002

Sep-2002Nov-2002

Jan-2003

Mar-2003

May-2003

Jul-2003

Sep-2003Nov-2003

Jan-2004Mar-2004

May-2004

Jul-2004

Sep-2004Nov-2004

Jan-2005Mar-2005

May-2005

Jul-2005

Sep-2005Nov-2005

Jan-2006

Mar-2006

May-2006

Jul-2006

Sep-2006Nov-2006

Jan-2007

Mar-2007

May-2007

Jul-2007

Sep-2007Nov-2007

Jan-2008

0.5

1

1.5

2

2.5

3

3.5

4

Dollars per Gallon

Diesel Price

Figure 3.1: Monthly Indices of Farm Prices (Base Year= 1992-1994), January 

1997-March 2008.

Figure 3.2: Average Monthly U.S. No. 2 Diesel Retail Price for All Sellers, 

January 1998-February 2008.
11

portation costs are incurred in delivering 

inputs and raw materials.

Fertilizer prices have moved in concert 

with rising energy prices. Figure 3.3 shows 

the average annual price paid for five selected fertilizer formulations over the past 

10 years. The costs per ton of anhydrous 

ammonia and urea have more than doubled 

since 1998, and ammonium nitrate is within $4/ton of doubling, as well. Since 1998, 

the average prices paid for diammonium 

phosphate and potassium chloride have increased by 67.4 percent and 84.2 percent, 

respectively. Although the 2006 to 2007 

average change in price paid for anhydrous 

ammonia is minimal, the increase from 

2005 to 2007 is just over $100/ton, a 20.5 

percent jump. In 2007, the average price 

paid for diammonium phosphate, another 

major fertilizer blend, increased $105/ton 

over the 2006 average price, a 31.2 percent 

increase. The average price paid for urea increased by $91/ton from 2006 to 2007, a 

25.1 percent increase.

An analysis of costs of production for 

the 2006 growing season and projected 

2008 costs was completed to illustrate the 

increasing expenses farmers face in the 

rapidly changing business of production 

agriculture. Crop budgets provided by the 

Texas AgriLife Extension Service teamed 

with rates of change for input prices from 

USDA-NASS were utilized to demonstrate 

increases in costs of production experienced 

by producers. Budgets for wheat, corn, cotton, and grain sorghum were obtained for 

the 2006 growing season for three major production regions in Texas. Rates of 

change for major inputs were calculated 

using January 2006 and January 2008 USDA-NASS monthly Index of Prices Paid 

by farmers (Table 3.1). These inflation rates 

were used to adjust 2006 direct and fixed 

expenses found in the extension budgets, 

estimating the changes in costs of production experienced over the last two years. The 

following is a more detailed description of 

the results of this analysis.

Wheat budgets were examined for 

dryland and irrigated production in the 

Texas Panhandle along with a dryland sys1995 1997 1999 2001 2003 2005 2007

100

200

300

400

500

600

Dollars per Ton

Anhydrous ammonia

Urea 45-46% nitrogen

Ammonium nitrate

Diammonium phosphate (18-46-0)

Potassium chloride 60% potassium

Figure 3.3: Average Annual Prices Paid for Major Fertilizer Formulations, 

1998-2007.

Table 3.1: Rates of Change for 

Prices Paid for Inputs.

Input Category Change

Percent

Seeds 23.4

Mixed Fertilizer 45.0

Insecticides 9.2

Herbicides 5.8

Services: Custom Rates 1.6

Fuels 37.1

Wage Rates 6.9

Fuels 37.1

Repairs 5.4

Services: Other Services 8.5

Interest 18.0

Cash Rent 17.5
12

Table 3.2: Costs of Production for Wheat in Major Production Regions of Texas, 2006 and 2008.

Panhandle (Dryland) Panhandle (Irrigated) Central Texas

2006 2008 2006 2008 2006 2008

Direct Expenses ($/acre)

 Seed 7.00 8.64 10.50 12.96 13.50 16.66

 Fertilizer 0.00 0.00 33.50 48.58 35.30 51.19

 Insecticide 0.00 0.00 0.00 0.00 4.50 4.91

 Herbicide/Fungicide 0.00 0.00 0.00 0.00 15.60 16.50

 Custom 20.62 20.95 42.70 43.38 25.75 26.16

 Irrigation Energy 0.00 0.00 150.00 205.65 0.00 0.00

 Labor 7.44 7.95 16.39 17.52 8.32 8.89

 Fuel 9.21 12.63 40.63 55.70 12.33 16.90

 Repairs 8.42 8.87 9.73 10.26 7.10 7.48

 Crop Insurance 0.00 0.00 0.00 0.00 5.31 5.76

 Operating Interest 2.42 2.72 13.96 18.13 5.87 7.11

Total Direct Expenses 55.11 61.76 317.41 412.17 133.58 161.57

Fixed Expenses ($/acre)

 Implements 6.75 7.32 7.81 8.47 8.57 9.29

 Tractors 7.24 8.25 9.13 10.40 8.31 9.47

 Self-Propelled Equipment 0.00 0.00 0.00 0.00 0.00 0.00

 Pickup 0.28 0.30 0.28 0.27 0.00 0.00

 Center Pivot 0.00 0.00 33.60 36.18 0.00 0.00

Total Fixed Expenses 14.27 15.86 50.82 55.32 16.88 18.75

Total Specified Expenses ($/acre) 69.38 77.62 368.23 467.49 150.46 180.33

Allocated Cost Items ($/acre)

 Land Charge 15.00 18.00 45.00 53.00 25.00 29.00

Total Expenses ($/acre) 84.38 95.62 413.23 520.49 175.46 209.33

Expected Yield (bu.) 18.00 18.00 65.00 65.00 45.00 45.00

Cost of Production ($/bu.) 4.69 5.31 6.36 8.01 3.90 4.65

tem in Central Texas (Table 3.2). The irrigated Panhandle budget experienced the 

largest increase in production costs per acre. 

The net increase from 2006 to 2008 is expected to be slightly more than $100/acre, 

a 26 percent increase in expenses. Natural 

gas is used to fuel the power units for irrigation, thus the increased cost for irrigation energy alone is in excess of $55/acre. 

Panhandle dryland wheat experiences the 

lowest increase in expenses at $11.24/

acre, a 13.3 percent jump. Central Texas 

dryland wheat is expected to cost just under $34/acre more to produce in 2008 as 

compared to 2006. When taking expected yields into account, the irrigated Texas 

Panhandle budget yields the highest cost 

of production of the three examined for 

2008 at 8.01/bu. The Central Texas budget estimates 2008 cost of production at 

$4.65/bu., the lowest of the three evaluated. 

Corn production expenses were examined under irrigation in the Texas 
13

Panhandle and under dryland practices 

in Central Texas and the Texas Coastal 

Bend (Table 3.3). The largest increase 

in production cost is experienced in the 

Texas Panhandle. Expected costs for 

producing irrigated corn in this region 

are expected to climb more than $165/

acre, a 23.2 percent increase over 2006. 

Corn producers in Central Texas and in 

the Texas Coastal Bend are expected to 

experience similar increases in expenses 

at $56.58 and $49.88, respectively. On a 

per yield unit basis, the 2008 cost of production is highest in the Texas Panhandle ($4.21/bu.) and lowest in the Texas 

Coastal Bend ($2.89/bu.) for the three regions evaluated.

Cotton production expenses from 

2006 to 2008 were evaluated for irrigated 

and dryland acreages in the Texas Panhandle along with dryland practices in two 

other regions of the state, Central Texas 

and the Texas Coastal Bend (Table 3.4). 

Table 3.3: Costs of Production for Corn in Major Production Regions of Texas, 2006 and 2008.

Panhandle (Irrigated) Central Texas Coastal Bend

2006 2008 2006 2008 2006 2008

Direct Expenses ($/acre)

 Seed 50.75 62.63 26.50 32.70 19.36 23.89

 Fertilizer 91.20 132.24 55.00 79.75 55.50 80.48

 Insecticide 23.50 25.66 14.41 15.74 6.26 6.84

 Herbicide/Fungicide 36.60 38.72 15.78 16.70 26.72 28.27

 Custom 69.60 70.71 4.00 4.06 15.96 16.22

 Irrigation Energy 210.00 287.91 0.00 0.00 0.00 0.00

 Labor 17.48 18.69 9.38 10.03 6.98 7.46

 Fuel 9.14 12.53 20.77 28.48 12.35 16.93

 Repairs 53.20 56.07 13.40 14.12 10.16 10.71

 Crop Insurance 15.00 16.28 8.77 9.52 6.40 6.94

 Operating Interest 14.41 18.04 9.24 11.61 7.35 9.10

Total Direct Expenses 590.88 739.48 177.25 222.70 167.04 206.83

Fixed Expenses ($/acre)

 Implements 9.54 10.34 12.63 13.69 8.50 9.21

 Tractors 7.82 8.91 12.16 13.85 11.72 13.35

 Self-Propelled Equipment 0.00 0.00 5.87 6.25 11.48 12.23

 Pickup 0.28 0.27 0.00 0.00 0.00 0.00

 Center Pivot 33.60 36.18 0.00 0.00 0.00 0.00

Total Fixed Expenses 51.24 55.70 30.66 33.79 31.70 34.79

Total Specified Expenses ($/acre) 642.12 795.18 207.91 256.49 198.74 241.62

Allocated Cost Items ($/acre)

 Land Charge 75.00 88.00 45.00 53.00 40.00 47.00

Total Expenses ($/acre) 717.12 883.18 252.91 309.49 238.74 288.62

Expected Yield (bu.) 210.00 210.00 90.00 90.00 100.00 100.00

Cost of Production ($/bu.) 3.41 4.21 2.81 3.44 2.39 2.89
14

Of the four budgets examined, the greatest 

increase in production expenses took place 

on the Texas Panhandle irrigated budget 

with an increase of $110.81/acre, a 17.2 

percent increase. The Texas Panhandle 

dryland budget increased expenses by only 

$28.92/acre, the lowest of the four budgets. 

This system is very low input, spending no 

money on harvest aids and only allocating 

the $6/acre boll weevil eradication assessment for insecticide expenses. Although 

this budget experienced the lowest increase 

on a per acre basis, it remains the highest 

on a per yield unit basis due to the relatively 

low expected yield of 300 lbs./acre. In 2008, 

the Central Texas and Texas Coastal Bend 

budgets are expected to increase spending 

over 2006 estimates by $68.10 and $58.99, 

respectively.

The final crop budgets evaluated were 

for grain sorghum under irrigation in the 

Texas Panhandle and under dryland conditions in Central Texas and the Texas Coastal 

Bend (Table 3.5). Estimated production cost 

Table 3.4: Costs of Production for Cotton in Major Production Regions of Texas, 2006 and 2008.

Panhandle (Dryland) Panhandle (Irrigated) Central Texas Coastal Bend

2006 2008 2006 2008 2006 2008 2006 2008

Direct Expenses ($/acre)

 Seed 7.20 8.88 48.00 59.23 69.00 85.15 61.56 75.97

 Fertilizer 17.90 25.96 46.75 67.79 50.00 72.50 48.26 69.98

 Insecticide 6.00 6.00 12.00 12.00 33.75 35.64 33.62 34.85

 Herbicide/Fungicide 0.00 0.00 18.50 19.57 42.48 44.94 37.46 39.63

 Custom 96.47 98.01 155.56 158.05 114.00 115.82 149.50 151.89

 Irrigation Energy 0.00 0.00 120.00 164.52 0.00 0.00 0.00 0.00

 Labor 20.01 21.39 24.80 26.51 11.04 11.80 6.71 7.17

 Fuel 15.49 21.24 18.21 24.97 22.34 30.63 9.44 12.94

 Repairs 26.35 27.77 48.86 51.50 11.69 12.32 5.10 5.38

 Crop Insurance 12.25 13.29 17.25 18.72 10.00 10.85 13.00 14.11

 Operating Interest 7.18 7.92 16.60 19.62 10.74 12.37 16.56 18.70

Total Direct Expenses 208.85 230.47 526.53 622.48 375.04 432.03 381.21 430.61

Fixed Expenses ($/acre)

 Implements 22.85 24.77 20.92 22.68 12.93 14.02 8.44 9.15

 Tractors 19.11 21.77 18.24 20.78 14.54 16.56 9.56 10.89

 Self-Propelled Equipment 0.28 0.00 0.00 0.00 0.01 0.01 8.40 8.95

 Pickup 0.00 0.00 0.49 0.48 0.00 0.00 0.00 0.00

 Center Pivot 0.00 0.00 33.60 36.18 0.00 0.00 0.00 0.00

Total Fixed Expenses 42.24 46.54 73.25 80.11 27.48 30.59 26.40 28.98

Total Specified Expenses ($/acre) 251.09 277.00 599.78 702.59 402.52 462.62 407.61 459.60

Allocated Cost Items ($/acre)

 Land Charge 15.00 18.00 45.00 53.00 45.00 53.00 40.00 47.00

Total Expenses ($/acre) 266.09 295.00 644.78 755.59 447.52 515.62 447.61 506.60

Expected Yield (lbs.) 300.00 300.00 850.00 850.00 600.00 600.00 750.00 750.00

Cost of Production ($/lb.) 0.89 0.98 0.76 0.89 0.75 0.86 0.60 0.68
15

increases are over $100/acre for the irrigated 

Panhandle budget in 2008, representing a 

21.9 percent increase in expenditures over 

2006 numbers. The next highest increase 

in expenditures is expected in the Texas 

Coastal Bend with an expected increase of 

$44.68/acre. The lowest expected increase 

is on the Central Texas budget, where an 

expected increase of almost $35/acre represents a 17.7 percent increase over 2006 expenditures. The Texas Coastal Bend budget 

slightly edges out the Central Texas budget 

for lowest cost of production on a per hundredweight basis at $4.87/cwt.

Producers growing irrigated crops and 

those employing more intensive production 

practices are more exposed to the increases 

in input prices; however, in an environment 

of rising prices of all inputs, no producers 

are completely insulated. The degree of impact on individual producers will vary, but 

all producers face rising costs that threaten 

bottom lines.

Table 3.5: Costs of Production for Grain Sorghum in Major Production Regions of Texas, 2006 and 2008.

Panhandle (Irrigated) Central Texas Coastal Bend

2006 2008 2006 2008 2006 2008

Direct Expenses ($/acre)

 Seed 6.25 7.71 8.40 10.37 6.58 8.12

 Fertilizer 52.50 76.13 30.30 43.94 49.95 72.43

 Insecticide 0.00 0.00 23.00 25.12 14.29 15.60

 Herbicide/Fungicide 0.00 0.00 9.75 10.32 23.51 24.87

 Custom 71.51 72.65 28.25 28.70 19.96 20.28

 Irrigation Energy 140.00 191.94 0.00 0.00 0.00 0.00

 Labor 17.56 18.77 5.59 5.98 6.98 7.46

 Fuel 11.25 15.42 10.69 14.66 12.35 16.93

 Repairs 43.67 46.03 8.02 8.45 10.16 10.71

 Crop Insurance 0.00 0.00 4.34 4.71 5.70 6.19

 Operating Interest 10.73 13.42 5.45 6.47 6.68 8.16

Total Direct Expenses 353.47 442.07 133.79 158.70 156.16 190.75

Fixed Expenses ($/acre)

 Implements 12.58 13.64 10.00 10.84 8.50 9.21

 Tractors 12.87 14.66 7.33 8.35 11.72 13.35

 Self-Propelled Equipment 0.28 0.30 1.02 1.09 11.48 12.23

 Pickup 0.00 0.00 0.00 0.00 0.00 0.00

 Center Pivot 33.60 36.18 0.00 0.00 0.00 0.00

Total Fixed Expenses 59.33 64.78 18.35 20.28 31.70 34.79

Total Specified Expenses ($/acre) 412.80 506.85 152.14 178.97 187.86 225.54

Allocated Cost Items ($/acre)

 Land Charge 75.00 88.00 45.00 53.00 40.00 47.00

Total Expenses ($/acre) 487.80 594.85 197.14 231.97 227.86 272.54

Expected Yield (cwt.) 70.00 70.00 45.00 45.00 56.00 56.00

Cost of Production ($/cwt.) 6.97 8.50 4.38 5.15 4.07 4.87
16

4 Livestock 

Production 

Costs

An important measure of the impact 

of ethanol and other costs on the livestock 

industry is to examine changes in production costs. Costs include more than feed, 

although feed is often the largest cost for 

livestock producers and feeders. This section examines production cost changes for 

the livestock industry from 2006 to 2008. 

This time period was chosen to capture 

the time period of increased corn prices 

due to expanded demand for corn from 

ethanol. Several different data sources are 

used. Those are noted in the text. 

Dairy

 The analysis of dairy production 

costs uses the representative Texas dairy 

farms maintained by the Agricultural and 

Food Policy Center (AFPC). The four 

dairies are a 1,300 cow Central Texas 

dairy, two East Texas dairies with 450 

and 1,000 cows, and a 3,000 cow North 

Texas dairy. 

On each dairy, feed is the largest cost, 

including both purchased and raised on 

the dairy. Feed costs ranged from 53 percent of costs on the 450 cow East Texas 

dairy to 62 percent of costs on the North 

Texas dairy. Over the 2006 to 2008 period, feed costs increased by 17 percent 

(450 cow East Texas dairy) to 22 percent 

(3,000 cow North Texas dairy) across 

these dairies (Figure 4.1). Only fuel and 

utilities costs increased at a faster rate (23 

percent). The increase in feed costs result 

in the dairies being even more subject to 

feed cost changes by 2008, in that feed 

makes up an even larger share of total 

costs. 

Of more importance is the change 

in total production costs over this period. 

The 450 cow East Texas dairy’s production costs per cwt of milk increase from 

$15.18 to $16.84 per cwt. Of that $1.66 

$17.29 $16.84

$16.08

$16.80

$15.48 $15.18

$14.37 $14.84

TXCD1300 TXED450 TXED1000 TXND3000

$0.00

$5.00

$10.00

$15.00

$20.00

Dollars per Hundredweight

2006 2008

$169.92

$58.27

$11.38

$55.46 $39.95

$277.69

$612.66

$146.09

$39.09

$10.69

$45.16 $55.73

$265.93

$562.70

Cattle Costs Cattle Feed Labor Utilities Interest Other Total

$0.00

$100.00

$200.00

$300.00

$400.00

$500.00

$600.00

$700.00

Dollars per Head

2006 2008

Figure 4.1: Dairy Production Costs.

Figure 4.2: Texas Representative Ranch Cow-Calf Production Costs.
17

per cwt increase, $1.19 is due to feed 

costs, and the remainder is due to other 

dairy costs, labor, utilities, and interest. 

The 1,000 cow East Texas dairy experiences production costs increasing from 

$14.37 to $16.08 per cwt. Costs increase 

from $15.48 to $17.29 per cwt on the 

Central Texas dairy, with feed costs increasing $1.26 per cwt. Total costs on the 

North Texas dairy increase $2.06 per cwt 

to $16.90. It is important to note that 

higher costs can be mitigated by increases 

in productivity. Production per cow increased at slightly less than 2 percent per 

year which is consistent with historical 

increases in productivity. 

Cost increases on these representative dairies range from about $1.50 to 

$2.00 per cwt. While milk prices have 

remained high due to export demand, 

increased milk production is expected to 

lower milk prices later in 2008. In practical terms, these results indicate that milk 

prices do not have to decline historical levels to create financial hardship on 

dairies which would jeopardize economic 

growth in the Texas Panhandle and further accelerate the exit of dairies in other 

areas of the state.

Cattle

For the purposes of this study, the 

cattle sector is divided into two parts, the 

cow-calf sector and the feedlot sector. 

Cow-Calf

Texas is the largest cow-calf producing state in the United States. It is common for ranchers to feed hay over the 

winter and supplemental feed at times 

throughout the year. Their total costs are 

less dependent on purchased feed costs 

than other production costs. However, 

feed production for the ranch may inJAN

APRJULOCT

2

3

4

5

6

Dollars per Bushel

Avg. 2002-06 2007 2008

Figure 4.3: Texas Triangle Corn Prices, 2006-2008.

clude costs of cutting and baling hay and 

fertilizing pastures. Production costs are 

analyzed using one representative ranch 

developed by the AFPC; a 500 cow ranch 

in the Rolling Plains. 

From 2006 to 2008 the ranch experienced a significant increase in costs. Total 

expenses on the Rolling Plains ranch increased by about $100 per cow from 2006 

to 2008 (Figure 4.2). Feed costs increased 

by about $20 per cow and cattle costs 

increased by about $23 per cow. Cattle 

costs include production costs such as 

fuel, fertilizer, transportation, marketing costs, and labor. Feed costs increased 

from about 7 percent of cash costs to 9.5 

percent of cash costs in 2008.

Cattle Feeding

Texas is also the largest cattle feeding state in the United States. Most of 

the cattle feeding industry is located in 

the Panhandle, but feedlots are also located in South Texas. Several sources of 

data are used to examine the impact of 

corn costs on the cattle feeding industry, 

including feed cost data published by the 

Agricultural Marketing Service (AMS), 

cattle feeding cost closeout data gathered 

and reported by Kansas State University, 

and cattle feeding returns estimated by 

the Livestock Marketing Information 

Center. The time period 2006 through 

2008 is used in this analysis to capture 

the effects of higher corn prices. 

The Texas Triangle is a common reference point for corn prices reported by 

AMS daily and weekly. The area includes 

the Plainview to Canyon area of the state. 

Texas Triangle corn prices averaged $2.32 

per bushel in January 2006 (Figure 4.3). 

In January 2008, corn prices averaged 

$4.95 per bushel, an increase of $2.63 

per bushel, or 113 percent. Even though 

Texas is a corn deficit state and sources a 

significant amount of grain out of state, 
18

this market is a good reference point for 

corn prices paid by feedlots.

Feed cost is the largest cost in the 

cattle feeding process (after the feeder 

steer or heifer purchase), accounting for 

approximately 78 percent of non-feeder 

costs. The remaining costs reflect interest, death loss, other operating costs, and 

labor (LMIC). The LMIC cattle feeding 

returns data series is calculated using feed, 

feeder steer, and fed steer prices to estimate returns to cattle feeding in Western 

Kansas. In this series, feed costs increased 

from $119.58 per head in January 2006 

to $216.46 per head in January 2008, or 

$96.88 per head (Figure 4.4). It is important to note that this series calculates feed 

costs by multiplying the average price for 

each month by the amount fed for each 

month in the feeding process. Because of 

this, during recent weeks of rapidly increasing corn prices, the higher prices are 

not immediately priced into total feed 

costs. Updating these feed costs to reflect 

Texas Triangle corn prices and finished 

weights estimates feed costs at $284.48 

per head in January 2008, a $129.81 per 

head increase since January 2006. 

However, in this fast moving corn 

market, prices have further increased. 

Texas Triangle corn prices were reported 

to be $5.96 per bushel for the week ending April 3, 2008. That is $1.02 per bushel 

higher than the average in January. Using 

the same cattle feeding returns estimate 

yields a projected feed cost of $357.34 

per head for April 2008. 

 Kansas State University has published a feedlot closeout series for many 

years called “Focus on Feedlots” (Kuhl). 

This data is based on a monthly survey 

of 6-10 feedlots. The reported January 

2008 average steer cost of gain per cwt 

$9.52

($171.54)

$154.67

$284.48

2006 2008

($200.00)

($100.00)

$0.00

$100.00

$200.00

$300.00

$400.00

Dollars per Head

Feed Costs Returns

Figure 4.4: Cattle Feeding Costs and Returns, January 2006 and 2008.

was $74.11. January, 2006 average cost of 

gain was $52.88.

Costs of gain in feeding cattle have 

increased significantly due to the increase 

in feed costs. Breakeven fed cattle prices 

have increased from $94.22 to $106.74 

per cwt as feed costs have increased. 

Normally, feeder cattle prices decline as 

feed costs increase. However, in this market, due to tighter supplies of cattle and 

the competitive structure of the market, 

feeder cattle prices have declined, but so 

have fed cattle prices. Texas combined 

auction feeder steer prices have declined 

from $110 to $98 per cwt from January, 

2006 to January, 2008. Fed steer prices 

declined from $94 to $92 per cwt over 

the same reference period. The result is 

higher production costs, and the inability 

to pass on those costs, resulting in higher 

losses in the industry.

Hogs

The hog industry has had a long run 

of profitable feed costs and prices. However, recent market conditions and rising 

feed costs have changed the profit picture 

dramatically. This analysis uses Iowa State 

University Extension Service’s Estimated 

Returns for Farrowing and Finishing 

Hogs in Iowa for the same reference period, January 2006 and January 2008 that 
19

$4.27

($33.38)

$99.86

$137.82

2006 2008

($50.00)

$0.00

$50.00

$100.00

$150.00

Dollars per Head

Production Costs Returns

Figure 4.5: Hog Finishing Production Costs and Returns. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug

Sep

Oct

NovDec

Jan

Feb

80

100

120

140

160

180

200

Index 1988-92=100

broiler eggs

Figure 4.6: Broiler and Egg Ration Cost Index, 2006-2008.

was used in the cattle feeding returns 

analysis.

The ISU data indicates corn costs 

to finish a 260 pound hog at $17.77 per 

head for pigs finished in January 2006. 

Feed and supplements other than corn 

accounted for $21.87 per finished pig. 

Feed made up 66 percent of the total finishing costs per head. By November, corn 

costs had surpassed the cost of the other 

feeds and supplements in the total cost 

of finishing. Corn costs in January 2008 

totaled $35.60 per head, more than double the corn costs of January 2006. The 

breakeven price increased from $38.41 to 

$51.04 per cwt. The estimated profit per 

head decreased from $4.27 to -$33.38 in 

January 2008 (Figure 4.5). 

While this analysis uses Iowa production costs and returns, the estimates 

should make a good comparison for Texas 

production. Corn is priced at a premium 

to move it to Texas as it is a corn deficit 

state. Higher corn prices have resulted in 

major financial losses to hog producers 

while the hog market has not yet begun 

the adjustment to higher costs.

Poultry

Broiler, turkey, and egg producers have all been affected by higher feed 

costs. USDA publishes an index of feed 

costs for producing broilers and eggs 

(Figure 4.6). Broiler feed costs have increased from an index value of 93.5 in 

January 2006 to an index value of 144.3 

in January 2008. The feed cost index for 

egg production has increased even more 

dramatically, from 99.7 in January 2006 

to 171.0 in January 2008.
20

5

Impact 

on Retail 

Prices – 

Farm Share 

of Retail 

Prices

As media outlets across the country 

have reported, retail food prices have increased, in some cases rapidly, over past 

few years. Table 5.1 contains the annual 

change in retail food prices for various 

products in the United States. Across 

all products, retail food price inflation 

has risen from 2.4 percent in 2005 and 

2006 to 4 percent in 2007 with the same 

increase expected for 2008. Other than 

eggs, there does not appear to be large 

deviations from price changes experienced in previous years. In 2008, the fats 

and oils and cereals categories are expected to see much higher than normal 

increases in costs. 

The farm level value of the raw commodity that is contained in retail food 

products is generally referred to as the 

farm share of retail food prices. While 

the latest data does not cover the current period of rapidly rising farm level 

prices, a few observations can be made 

with regard to the likely impact on retail 

food prices. Table 5.2 illustrates that, on 

average, the farmer’s share of retail food 

prices has declined from 32 percent in 

1970 to 19 percent in 2002. Also evident from the data is that, in general, the 

farm share of retail food prices tends to 

decline as agricultural commodities are 

further processed into food products. 

For example, the farm share of retail 

food prices for fresh vegetables in 2002 

was 20 percent while the farm share of 

retail food prices for bakery and cereal 

products was 5 percent. While not definitive, the USDA data would provide 

some support to the notion that the inTable 5.1: Annual Change in Consumer Price Index for Selected Food 

Categories, 2004 to 2008.

2004 2005 2006 2007 2008 

(forecast)

Consumer price indexes Percent Change

All food 3.4 2.4 2.4 4.0 3.0 to 4.0

 Food at home 3.8 1.9 1.7 4.2 3.5 to 4.5

 Meats, poultry, and fish 7.4 2.4 0.8 3.8 2.0 to 3.0

 Meats 8.4 2.3 0.7 3.3 1.5 to 2.5

 Beef and Veal 11.6 2.6 0.8 4.4 2.0 to 3.0

 Pork 5.6 2.0 -0.2 2.0 1.5 to 2.5

 Other meats 4.5 2.4 1.8 2.3 0.0 to 1.0

 Poultry 7.5 2.0 -1.8 5.2 1.5 to 2.5

 Fish and seafood 2.3 3.0 4.7 4.6 3.0 to 4.0

 Eggs 6.2 -13.7 4.9 29.2 0.0 to 1.0

 Dairy products 7.3 1.2 -0.6 7.4 2.0 to 3.0

 Fats and oils 6.6 -0.1 0.2 2.9 6.5 to 7.5

 Fruits and vegetables 3.0 3.7 4.8 3.8 3.0 to 4.0

 Fresh fruits & vegetables 3.5 3.9 5.3 3.9 3.0 to 4.0

 Fresh fruits 2.8 3.7 6.0 4.5 3.5 to 4.5

 Fresh vegetables 4.3 4.0 4.6 3.2 2.5 to 3.5

 Processed fruits & vegetables 1.3 3.3 2.9 3.6 3.0 to 4.0

 Sugar and sweets 0.7 1.2 3.8 3.1 2.0 to 3.0

 Cereals and bakery products 1.6 1.5 1.8 4.4 5.5 to 6.5

 Nonalcoholic beverages 0.4 2.9 2.0 4.1 3.5 to 4.5

 Other foods 0.5 1.6 1.4 1.8 2.5 to 3.5

Source of historical data: Bureau of Labor Statistics; forecasts by Economic Research Service. 

Labor

Farm value Marketing bill

Profit

Advertising

Depreciation 

s

Packaging

Transportation

Energy

Repairs

Business taxes

Rent

Interest

Other costs

19¢ 38.5¢ 8.0¢ 4.0¢ 3.5¢ 4.5¢ 4.0¢ 3.5¢ 4.5¢ 2.5¢ 1.5¢ 3.5¢ 3.0¢

Source: ERS.

Figure 5.1: Illustration of the Farm Share and Components of the Marketing 

Bill for U. S. Retail Food Products.
21

Table 5.2: Farm Share of the Retail Value of Selected Categories of Agricultural Products, 1970 to 2002.

Year Fresh Vegetables Processed Fruit 

and Vegetables

Bakery and Cereal 

Products

Average for All 

Farm Products

Percent

1970 32 19 16 32

1971 33 18 16 32

1972 32 19 17 33

1973 35 19 22 37

1974 34 22 25 36

1975 35 21 18 33

1976 33 20 15 32

1977 33 18 12 30

1978 30 25 13 32

1979 28 24 14 32

1980 27 23 14 31

1981 32 23 13 28

1982 34 24 12 27

1983 34 23 12 27

1984 34 24 12 27

1985 31 26 11 25

1986 28 23 8 25

1987 31 24 8 24

1988 28 28 9 24

1989 29 25 9 25

1990 28 26 8 24

1991 24 22 7 22

1992 26 23 8 22

1993 26 19 7 22

1994 23 20 8 21

1995 23 21 8 21

1996 20 20 9 22

1997 21 19 7 22

1998 20 18 6 20

1999 19 17 6 20

2000 19 17 5 19

2001 19 16 5 19

2002 20 16 5 19

Source: Calculated by ERS based on data from government and private sources.

crease in farm level corn prices should 

be impacting retail food prices very 

little – primarily because corn is generally consumed as an ingredient in highly 

processed foods such as HFCS in soft 

drinks, cereals, sauces, and hundreds of 

other products. The same type of generalization is expected to hold for the impact of rising wheat prices on the price 

of bread, cereal, and other bakery products.

A number of news stories have been 

written that cite recent increases in the 

farm level prices of corn, grain sorghum, 

wheat, soybeans, and rice as causing significant increases in retail food prices. 

Based on the examples provided earlier, 

it is clear that while some of the increase 

in retail food prices is due to farm level 

price increases, there are likely a number 

of causes for higher retail food prices. 

Figure 5.1 displays the components of 

the marketing margin for retail food 

products. Again, based off of 2002 data, 

the portion of a dollar spent on retail 

food products averages $0.19 while labor accounts for $0.38. One element 

to rising food prices that tends to be 

overlooked is the impact of higher fuel 

prices (oil and natural gas) have on retail food prices. The impacts of higher 

energy prices would be felt throughout 

several categories in the marketing bill 

but would be primarily in the packag-
22

Table 5.3: Farm Commodity Prices and Farm Value in Typical Retail Food Products.

Farm Commodity 2004 Farm Price 2007 Farm Price Retail Product Farm Cost of Retail 

Product in 2004

Farm Cost of Retail 

Product in 2007

Corn $2.06/bu $4.00/bu HFCS in Soda $0.115/12-pack $0.222/12-pack

Cotton $0.416/lb $0.535/lb Dress Shirt $0.26/shirt $0.33/shirt

Wheat $3.40/bu $6.65/bu Loaf of Bread $0.05/loaf $0.09/loaf

Wheat $3.40/bu $6.65/bu Donuts $0.056/dozen $0.109/dozen

Milk $16.13/cwt $19.15/cwt Gallon of Milk $1.38/gallon $1.65/gallon

Steer $0.8451/lb $0.9296/lb Pound of Beef* $0.8451/lb $0.9296/lb

Hog $0.51/lb $0.48/lb Pound of Pork* $0.51/lb $0.48/lb

*Reflects that the purchase price of a steer or hog is for the entire animal. The retail price is for a specific cut of meat that has a wide range of prices. 

For example, steaks at $6.99/lb and hamburger at (1.99/lb) each are obtained from the carcass but are sold at different prices while the live animal is 

sold at a constant price per pound.

ing ($0.08), transportation ($0.04), and 

energy ($0.04) categories.

Ag Value of Commodities 

The frequency of the questions regarding the agriculture value of popular 

products led us to develop the table below of our estimates of the farm value of a 

few commodities. Table 5.3 provides the 

farm level prices for selected agricultural 

commodities for 2004 and 2007; a typical retail food product that is made from 

each commodity; and the farmer’s share 

of the value of the selected retail product 

in 2004 and 2007. For example, a typical 

12-pack of soda contains roughly $0.11 

worth of corn when the price is $2.06 

per bushel and $0.22 worth of corn at 

$4.00 per bushel. As the table indicates, 

the farmer’s share of these selected retail products is very small compared to 

the overall retail cost. The examples 

for beef and pork are slightly different 

because of the nature of how beef and 

pork are priced. Each carcass is valued 

at the live animal price paid to feeders. 

That also ends up being the farmer’s (or 

feeders in this case) share per pound of 

the retail products including from low 

priced hamburger to much higher priced 

steaks.
23

6

Effects on 

Equilibrium 

Food 

Market 

Prices and 

Livestock 

Margins

Economists have scarcely begun to 

analyze the effects of recent, dramatic increases in the prices of agricultural inputs 

used in producing biofuels. Many organizations have recently examined the possible 

effects of these price increases on the costs 

of producing food (Informa, 2007; Urbanchuck, 2007; Henderson, 2008). These 

studies have generally conducted such inference using historical cost shares and the 

assumption of no substitution in production as the market prices of inputs change. 

For a practical example, it is well known 

that portion size changes and the mix of 

ingredients, like the amount of cheese on 

a pizza, changes in response to prices. This 

fixed proportions assumption is, or course, 

inappropriate in the long run. Furthermore, 

no consideration is given to the changes 

in the extent of production that would be 

compelled by evolving production profitability. Given these issues, the analyses cited above provide only rough evidence, even 

on the supply side.1

 Of course, those studies 

only provide limited information regarding 

possible effects on equilibrium prices, as the 

effects on market demands are not considered.

An analysis of the effects of crude oil 

price increases on biofuel production and 

agricultural market equilibria (including 

retail food prices), was undertaken by Tokgoz, et al. (2007). Specifically, they used a 

large-scale, partial equilibrium econometric 

model to examine the effects of a permanent increase in the crude oil price of $10 

per barrel, starting from a base price of approximately $65 per barrel. Based on their 

particular specification for the biofuels sector, this resulted in an increase of about 40 

percent in the long-run equilibrium price 

of corn. This, in turn, resulted in varying 

1

 A more careful analysis of the effects of input price 

changes (including agricultural inputs) on food processing costs is provided by Paul and MacDonald 

(2003).

t

N

n=

t t n y = A + ∑A y + e −

1

0 1

increases in the equilibrium retail prices of 

food items, with larger increases (though 

small in absolute terms) being realized in 

meat and dairy products.2

This section of this report takes a 

small step in understanding the effects of 

increasing biofuels production on equilibrium retail market prices for food and on 

meat production margins. We measure the 

effects of three putative causes of changes 

in equilibrium retail food prices that have 

been of particular concern in recent years – 

the prices of energy, labor, and agricultural 

biofuels inputs. In addition, we measure 

the effects of these same price changes on 

equilibrium margins at different stages of 

beef and pork production. For both retail 

food prices and meat production margins, 

we provide estimates of the specific effects 

of unexpected changes in energy, labor, and 

biofuels feedstock prices that have been 

realized since 2005, as biofuels production 

has increased.

We employ relatively simple time-series methods for this analysis. This provides 

a counter-point to the structural analysis 

of Tokgoz, et al. Also, our measurements 

of historical realized effects of energy, labor, and biofuels feedstock price changes 

provides a basis of comparison for the expected future effects that they estimate. 

Taking a relatively simple time-series approach is both a strength and a weakness 

of this analysis. Measurements of historical, 

realized average effects of increased commodity prices reflect market peculiarities 

that may not be fully reflected in large-scale 

structural models, such as shifting consumer preferences and the net effects of market 

power at various stages of production. We 

also enjoy a reduced risk of mis-specifying 

structural relationships, as we require only 

2

 All effects of energy price increases on retail food 

prices were indirect, being mediated by increases in 

the prices of agricultural commodities used in biofuels 

production.

very minimal assumptions in that regard. A 

weakness of our approach is that the historical data are just that, and as such our measurements do not fully reflect the structural 

change that has been taking place in recent 

years. More specifically, the indirect effects 

of increases in crude oil prices, mediated by 

corn prices, are likely under-represented in 

the models.

Methodology

We employ standard vector autoregression (VAR) and innovation accounting 

techniques. We describe these techniques 

very tersely; for an accessible treatment of 

these methods, see Enders (1995). For each 

margin or retail price that we consider, we 

specify a VAR model consisting of four 

variables, the price of crude oil, the price 

of labor, the price of corn, and the retail 

price or margin under consideration. Innovations, or changes, to the price of corn 

in these systems not only reflects the direct 

influence of increasing biofuels production, 

but, in the absence of other agricultural 

prices in these systems, serves as a proxy 

for the indirect effects being transmitted 

through the agricultural economy.

For each margin or retail price, we estimate a VAR in standard form

where yt

 is a 4x1 vector of variables observed 

in period t, the Ai

 are conformable parameter matrices, and et

 is a vector of correlated, 

zero-mean innovations for period t with 

E(ut

ut’) = ∑. Systems other than those containing the overall food at and away from 

home CPIs also contain seasonal harmonic 

variables. The standard VAR is an over-pa-
24

( )( ) r u LR= T −c log|Σ|−log|Σ|

rameterized, reduced-form representation 

of a dynamic structural model:

where εt

 is a 4x1 vector of uncorrelated 

disturbances. The standard from VAR 

can be written in a vector moving average 

representation, where the value of the endogenous variables are written strictly as 

functions of the history of the underlying, 

orthogonal structural disturbances:

where φi

 the parameter matrices reflect 

various elements of the B and Ai

 matrices from above. The sum over i of specific 

elements from the φi

 matrices provide an 

estimates of the long-run multipliers – the 

long-run effects on one variable of a shock 

to another. For example, the impact on egg 

prices of a shock to corn prices or, in other 

words, how egg prices are affected, in the 

long run, by a change in corn prices. In this 

application, we recover the εt

 series and estimates of the φi

 matrices from the underidentified standard VAR system using the 

Choleski decomposition of ∑. This implicitly assumes that the underlying structural 

model is recursive, as discussed further below. The vector moving average expression 

above can be partitioned to separate the effects of shocks occurring before and after a 

particular point in time:

where the term in parentheses represents 

the effects of shocks occurring before period t-s, and the term before the parentheses 

represents the effects of shocks occurring 

after. 

For the present application, a natural 

ordering of the variables and associated 

set of just-identifying restrictions suggests 

itself. We assume that the price of crude 

oil in one period is not affected by contemporaneous shocks in any of the other 

variables, the U.S. labor price is affected by 

contemporaneous crude oil shocks, the corn 

price could be affected by contemporaneous shocks from either oil or labor prices, 

and retail food prices are last. This ordering 

reflects the relative sizes of these markets, 

and lags in food production. The crude oil 

price is placed first, as the crude oil market 

is a very large, international market. The labor price, obviously, has great importance 

across many industries within the U.S., but 

will likely affect international energy prices 

only with delays. The corn price should naturally be ahead of retail food prices, given it 

is an input into production of livestock and 

consumer food products.

All VAR systems are estimated using 

OLS, with optimal lag lengths being determined by the Akaike information criterion. 

We heed the recommendations of Nerlove 

et al (1979), Sims (1980), and Doan (2004), 

and do not difference the data, even though 

non-stationarity could be present, so as not 

to lose information. However we do take 

care to ensure that the non-stationarity in 

the explanatory variables explains any the 

non-stationarity in the dependent variable, 

as Nerlove et al. suggest. Specifically, we 

check the recovered VAR residual series for 

non-stationarity using augmented DickeyFuller tests, finding they are stationary in 

all cases (results not shown). For each system, a block exogeneity test is conducted 

for each of our three putative determinants 

of meat production margins and retail food 

prices. Restricted systems in which coefficients on all lags of the variable being tested 

are zero are estimated, and a likelihood ratio test statistic is calculated:

where T is the number of observations, c is 

the total number of individual parameters 

being restricted, and ∑r

 and ∑u are the restricted and unrestricted error covariance 

matrices, respectively. This test statistic is 

distributed as c2

 with c degrees of freedom.

Data

We employ monthly observations of 

all variables. All systems include crude 

oil, labor, and corn prices. The crude oil 

price series we use is the spot price of 

West Texas Intermediate crude at Cushing, Oklahoma, collected from the Department of Energy. The labor price we 

employ is the Average Weekly Earnings 

of Production Workers (total private) as 

reported but the Current Employments 

Statistics (CES) survey conducted by the 

Bureau of Labor Statistics. The corn price 

is the spot price of number 2 yellow corn 

in central Illinois, collected from Primark 

Datastream.

In addition to those three variables, 

each system contains one meat processing or retailing margin series, a livestock 

price series, or a retail price series. Retail 

prices for eggs, bread, milk, tomatoes, and 

lettuce are the U.S. City Average Prices 

reported in the Bureau of Labor Statistics 

Average Price Data. Beef, pork, and broiler retail prices are reported by USDA. 

The Food at Home and Food Away From 

Home CPIs are the U.S. City Averages 

(Not seasonally adjusted; base period = 

1982-84) as reported by the Bureau of 

Labor Statistics in the Consumer Price 

t

N

n=

t n t n By = Γ + ∑Γ y + ε −

1

0

∑

∞

−

i=0

t i t i y = μ+ φ ε

∑ ∑

∞

− −

0 j =s+1

j t j

s

i=

t i t i y = φ ε + μ+ φ ε
25

Table 6.1: Block Exogeneity Tests for Retail Food Price Systems.

Egg price Bread 

price Milk Price Beef price Pork price Chicken price

Lettuce 

price

Tomato 

price

Food-athome CPI

Foodawayfrom-home 

CPI

Crude 

oil price 0.239 0.590 0.643 0.457 0.381 0.170 0.613 0.264 0.650 0.036

Labor 

price 0.005 0.040 0.002 0.293 0.039 0.014 0.000 0.000 0.064 0.000

Corn 

price 0.005 0.005 0.102 0.508 0.375 0.197 0.657 0.816 0.046 0.816

Table 6.2: Long-run Effects of Crude Oil, Labor, and Corn Price Shocks on Retail Food Prices.

Egg price Bread 

price Milk Price Beef price Pork price Chicken price

Lettuce 

price

Tomato 

price

Food-athome CPI

Foodawayfrom-home 

CPI

Crude 

oil price – – – – – – – – – 0.007

Labor 

price 0.355 0.364 0.373 – 0.140 0.154 0.339 0.399 0.146 0.000

Corn 

price 0.250 0.066 0.100 – – – – – 0.038 –

Index – All Urban Consumers (Current 

Series).

The feeder cattle price is the Amarillo monthly average 700-800 pound steer 

price reported by AMS. Fed cattle prices 

are the Texas-Oklahoma average fed 

steer price. The feeder-fed price margin is 

calculated as the value of a fed steer minus the value of the feeder steer. Live-tocutout and cutout-to-retail beef and pork 

data series are provided by the Livestock 

Marketing Information Center.

Effects on Equilibrium Retail Food 

Prices

P-values for block exogeneity tests 

are reported in Table 6.1. For all retail 

food prices considered, we cannot find a 

statistically significant effect of crude oil 

prices. We cannot find a significant effect of the labor price on equilibrium retail beef prices, but it is significant for all 

other commodities. We find a significant 

effect of the price of corn on egg, bread, 

and milk prices only. The food-at-home 

CPI is influenced significantly by the labor and corn prices, while the food-awayfrom home CPI is influenced significantly 

by the crude oil and labor prices. For each 

retail price considered, the corresponding 

final VARs from which long-run multipliers and historical decompositions are 

calculated exclude the series that we find 

are block exogenous.

Long-run multipliers are calculated 

at the 24-month horizon. The 24 month 

horizon indicates the cumulative effect 

of a change in oil prices, for example, on 

egg prices 24 months later. This lag allows 

for economic adjustments to occur to estimate the long term changes in prices. 

In Table 6.2, we report the effect on each 

equilibrium retail food price of a one percent shock in the crude oil, labor, or corn 

price. Effects are less than one percent in 

all cases. The results indicate that a one 

percent increase in corn price leads to a 

0.25 percent change in retail egg price at 

the end of 24 months. Or that a 10 percent increase in corn prices leads to a 2.5 

percent change in retail egg prices after 

24 months. 

While bread is not made with corn, 

it was modeled this way to capture the 

effect of higher corn prices – the primary 

feedstock for ethanol. The model indicates a relationship between corn and 

bread prices. The reason for this relationship is the indirect effects of tradeoffs between corn and wheat. Higher corn prices 
26

change production and use of other crops. 

That feedback is captured here.

Also of interest is the effect of labor prices on food. Labor price can also 

be thought of as wages. As labor prices 

increase, production costs increase. But, 

also, higher labor costs imply higher 

wages or incomes. Higher incomes affect 

the demand for food. It is interesting that 

shocks to labor costs did not significantly 

affect beef prices.

Decompositions of unexpected 

changes in retail food prices since January 2005 are presented in Table 6.3. The 

decomposition breaks up the total effect 

into its parts. For example, since 2005 unexpected egg price changes have amounted to a 27.6 percent change. Corn prices 

caused 6.4 percentage points, unexpected 

labor price increases caused 1.7 percentage points of increase, and other causes 

accounted for the remaining 19.5 percentage point change. In each food price, 

other factors accounted for the largest 

share of the percentage change in prices.

So, what constitutes other causes? It 

includes costs other than oil, labor, and 

Table 6.3: Decompositions of Unexpected Changes in Retail Prices since January 2005.

Egg price Bread 

price Milk Price Beef price Pork price Chicken price

Lettuce 

price

Tomato 

price

Food-athome CPI

Foodawayfrom-home 

CPI

Due to 

crude 

oil price 

shocks

– – – – – – – – – 0.1%

Due to 

labor 

price 

shocks

1.7% 1.9% 1.2% – 0.6% 0.7% 1.2% 1.3% 0.4% 0.0%

Due to 

corn 

price 

shocks

6.4% 4.6% 4.0% – – – – – 0.4% –

Due to 

other 

causes

19.5% 5.2% 6.6% -4.7% -2.3% 1.4% -4.9% -11.4% 0.5% 0.2%

Total 27.6% 11.7% 11.9% -4.7% -4.7% 2.1% -3.7% -10.1% 1.3% 0.4%
27

Table 6.4: Block Exogeneity Tests for Livestock Prices and Meat Production or Marketing Margin Systems.

Hog price Pork live-tocutout spread

Pork cutout-toretail spread

Texas-Oklahoma feeder steer 

price

Fed cattle – 

feeder steer 

price spread

Beef live-tocutout spreadBeef cutout-toretail spread

Crude oil price 0.022 0.028 0.738 0.098 0.241 0.354 0.461

Labor price 0.020 0.000 0.010 0.041 0.135 0.001 0.043

Corn price 0.544 0.920 0.804 0.154 0.035 0.745 0.751

Table 6.5: Long-Run Effects of Crude Oil, Labor, and Corn Price Shocks on Livestock Prices and Meat Production and Retailing Margins.

Hog price Pork live-tocutout spread

Pork cutout-toretail spread

Texas-Oklahoma feeder steer 

price

Fed cattle – 

feeder steer 

price spread

Beef live-tocutout spreadBeef cutout-toretail spread

Crude oil price 0.296 -0.432 – 0.179 – – –

Labor price 0.081 0.408 0.319 0.164 0.304 0.605 0.452

Corn price – – – – 0.173 – –

corn. Interest cost is one example. But, 

perhaps most important, are issues of industry structure and production cycles. 

For example, milk prices have increased 

due to export demand. But, competitive 

pressures and market environment dictate the ability to change prices. High 

prices do reduce quantity demanded and 

result in lower prices. Egg prices have increased sharply, but are the result of low 

prices and reduced supplies early in the 

period. All of the change in retail beef 

prices is due to other causes which could 

be changes in marketing margins. 

Effects on Equilibrium Beef and Pork 

Margins

The small effects of corn and oil prices on retail food prices led us to investigate effects on price spreads in livestock. 

Costs have already clearly increased to 

some sectors of the livestock industry, affecting profitability. A natural question is 

to what extent these price changes have 

affected margins in the livestock industry. P-values for block exogeneity tests 

for systems including livestock prices and 

meat production or marketing margins 

are reported in Table 6.4.

The labor price was found to be significant for each livestock price and price 

spread analyzed. That makes some sense 

given the large labor costs in the packing 

industry, represented by the live-to-cutout price spread. Oil prices had a significant effect on hog and feeder prices and 

the pork live-to-cutout spread. Corn only 
28

had a significant effect on the fed cattlefeeder cattle price spread.

Table 6.5 contains the long-run effect 

of changes in corn, oil, and labor prices on 

the livestock and meat price spreads. A one 

percent change in the corn price is estimated to lead to a 0.173 percent change in the 

fed-to-feeder price spread. That spread increased partially to offset the price of feed.

Decomposing the long run effects into 

their various parts yields the data contained 

in Table 6.6. The long run effects reveal, in 

some cases, offsetting effects on prices and 

price spreads. For example, hog prices have 

declined, but oil and labor comprise a 10 

percentage point increase partially offsetting the 13.3 percentage point decline from 

other causes. The same holds for Amarillo 

feeder steer prices.

This analysis indicates that high corn prices 

have had very little impact on retail food 

prices. Oil prices have also had very little 

effect on food prices. But, labor costs do 

significantly affect prices. Increased labor 

costs have two effects. They increase production costs and increase consumers’ incomes.

The lack of evidence of the impact of 

corn price changes on livestock prices and 

margins is critical in understanding the 

transition period underway. The analysis 

looks at a long run, 24 month adjustment 

period. In the case of cattle, corn price 

changes are thought to translate quickly to 

feeder cattle prices which would make the 

difference at 24 months insignificant. In 

fact, examining the shorter length effects 

indicate that there is a larger effect of corn 

prices early that declines to almost nothing by 24 months. It would appear that this 

research supports the hypothesis that the 

transition in livestock prices and margins 

has yet to move through the system.

Table 6.6: Long-Run Effects of Crude Oil, Labor, and Corn Price Shocks on Livestock Prices and Meat Production and Retailing Margins.

Hog price Pork live-tocutout spread

Pork cutout-toretail spread

Texas-Oklahoma feeder steer 

price

Fed cattle – 

feeder steer 

price spread

Beef live-tocutout spreadBeef cutout-toretail spread

Due to crude 

oil price 

shocks

9.0% -16.8% – 2.9% – – –

Due to Labor 

price shocks 1.0% 1.2% 1.1% 0.7% 2.3% 1.6% 2.2%

Due to corn 

price shocks – – – – 11.0% – –

Due to other 

causes -13.3% 1.2% -3.1% -10.1% 2.0% -7.8% 1.5%

Total -3.3% -14.4% -2.0% -6.4% 15.3% -6.2% 3.7%
29

7

Supply and 

Demand: 

Some 

Reasons for 

High Prices

Fundamentally, the prices of agricultural commodities depend on the relative 

supply and demand for those commodities. Supply and demand depend on many 

factors. Prices exhibit cyclical and seasonal 

patterns based on longer term characteristics (cycles) and normal supply and demand patterns during the year (seasonal).

All of Texas’ agricultural commodities have been affected to some degree by 

higher fuel costs and increased demand, 

and the resulting higher prices, for corn. 

But, each commodity has some underlying reasons for why prices are where they 

are today. Different fundamental causes by 

commodity affect the timing and severity of market corrections. The biological 

nature of production of each of the commodities affects the speed of adjustment. 

The structure of each of these sectors and 

the competitive market environment of 

each also affects the ability of industry 

segments to pass on cost increases. The following is a brief overview of major commodity markets. This overview is included 

in this report to aid readers who may not 

be involved in day to day agricultural markets.

Wheat

Wheat is most importantly a food 

grain, as opposed to a feed or fuel grain. 

Some wheat is fed to livestock when price 

relationships to corn allow it, but feed use 

is not typically large in any year. 

Recent record high wheat prices 

have been predominantly caused by supply problems in the United States and the 

world. In the U.S., fewer acres and a rain 

damaged harvest in the Southern Plains 

limiting supplies. A record drought in 

Australia sharply cut supplies from that 

major world supplier of wheat. Tighter 

supplies in other major producing regions 

of the world have been the major cause of 

record prices. 

Given that ethanol (demand) has not 

been the major cause of higher prices for 

wheat, this is one market that could experience a major downward correction 

in prices due to increased planted acres, 

worldwide. Price declines would be limited, however, by increased production costs 

and relative corn prices.

Corn

Historically, livestock have been the largest user of corn and feedgrains in the 

United States, followed by exports. Food, 

seed, and industrial corn use, the category 

that includes ethanol and high fructose 

corn syrup, has been the third largest user 

of corn. As ethanol production has increased, it has exceeded exports in use and 

is projected to exceed livestock feeding in 

the next decade to become the largest user 

of corn. 

While the increased demand for corn 

to make fuel is the largest reason for high 

corn prices, it is important to put the market situation in context. Some other market forces are at work in pushing prices 

higher.

Planted corn acres declined for the 

2006-2007 marketing year to 78.8 million acres, largely due to higher fertilizer 

and fuel costs. That was a 3 million acre 

decline from the prior year. Corn prices 

made their first large increase in the fall of 

2006 due to the realization of fewer acres 

planted and harvested and the rapid increase in demand from ethanol plants.

Planted acres surged to 93.6 million in 2007, to the largest number since 

World War II. Combined with today’s 

high yields, the harvested corn crop in 

2007 was the largest U.S. crop in history. 

Increased use has kept stocks historically 

tight and prices high. 

Increased corn acres came, largely, out 

of soybean acres. The resulting reduced 

soybean production led to sharp price 

increases for soybeans and a bidding war 

for acres to be planted in 2008, for the 

2008-09 marketing year. USDA’s prospective plantings report issued March 31, 

2008 indicated that only 86 million acres 

of corn would be planted in 2008, an 8 

percent reduction from 2007. 

With record high corn prices why 

would planted acres decline? The first reason is relative prices to soybeans. Compared to corn prices, soybean prices have 

been even higher, the market’s signal to 

farmers was to plant more soybeans relative to corn. Corn is often grown in the 

Corn Belt in a crop rotation with soybeans. Some producers may have had to 

switch some acres back to soybeans to 

maintain the benefits of their rotation patterns. Soybeans are a less expensive crop to 

grow than corn, which pulls more acres to 

soybeans. 

The last market factor to consider is 

exports. Exports are normally thought of 
30

as the most responsive to price. But, corn 

prices are high worldwide, limiting the 

ability of high prices in the U.S. to ration exports. The falling value of the dollar 

makes U.S. corn, relatively, less expensive 

to foreign buyers. 

Soybeans

Much of the increase in corn acres 

planted in 2007 came at the expense of 

soybean acres. The resulting high soybean, 

meal, and oil prices have encouraged more 

soybean acres in 2008. Some of the price 

increases in corn and soybeans are due to 

the market signals to bid acres into one 

crop or the other. These signals are felt 

more keenly in corn and soybeans due to 

the large majority of U.S. production of 

these crops being in the Midwest or Corn 

Belt. Those two crops probably more directly compete for acres than any other 

two crops in the country. 

Cattle

The cattle industry remains a cyclical 

industry. Drought in the Southern Plains 

in 2006 and the Southeast in 2007 forced 

contraction in the cow inventory. This liquidation phase has been aided by lower calf 

prices and higher production costs. Tight 

supplies of calves are acting to support calf 

prices in the face of higher feed costs.

The Southern Plains of the United 

States is the predominant wheat pasture 

area of the country. Wheat is planted in 

the fall and grazed over the winter. The 

calves are removed in March if the wheat 

is to be harvested or May if the wheat is to 

be grazed out. Record wheat prices, poor 

wheat stand establishment, and the short 

supply of wheat seed (critical if a producer 

had to re-plant) resulted in a sharp reduction in the availability of wheat pasture 

and the number of calves on wheat pasture. The result was more cattle placed on 

feed over winter, even though feed costs 

were increasing. 

More cattle imported from Canada 

have been placed on feed in recent months 

because their feed costs have increased 

even more than those in the U.S. Beef exports have increased to Canada in return 

as they produce less beef and as the weaker 

U.S. dollar encourages exports. 

The cattle and beef market might best 

be described at this time as a tug-o-war 

between high feed costs and the economy 

pushing prices down and tighter supplies 

of cattle supporting feeder cattle and calf 

prices.

Dairy

Milk prices hit record high levels in 

2007. Prices continued to increase even as 

production per cow and milk cow inventory increased. In this case, record prices 

were caused by increasing demand for 

milk products. Tight supplies in the rest 

of the world, as Australia contended with 

a drought and the EU reduced production 

as subsidies were cut, led to higher world 

prices and sharply higher U.S. exports. 

Milk prices can best be characterized as an 

export led expansion and not tighter supplies due to higher feed costs. However, 

moderating export demand and expanded 

production is reducing milk prices. Higher 

feed costs are increasing production costs, 

However, milk prices continue to fuel expansion in the number of milk cows, production per cow and total production.

Hogs

The hog industry has been in the 

midst of a long expansion due to profitable conditions. Industry consolidation 

and structural change has led to increased 

efficiencies supporting expansion. World 

markets have expanded for U.S. pork increasing exports and providing a market 

outlet for increased production. It is also 

generally agreed that more effective vaccines for circovirus have led to reduced 

mortality, further increasing production. 

Record large pork production is occurring simultaneously with sharply 

higher feed costs, leading to expectations 

of record financial losses in the production sector in 2008. While sow slaughter 

has increased over early 2007, productivity 

gains continue to increase, as well. 

Poultry

Broiler production in 2008 has been 

larger than in 2007 due to more birds 

slaughtered and heavier weights. In spite 

of large production, prices only recently 

have begun to fall befow year ago levels. 

Combined with higher feed costs, low 

prices are increasing the losses in the sector. Only leg quarter prices have remained 

higher, supported by exports.
31

8 Futures and 

Funds

The increase in speculative funds in agricultural commodities over the last couple 

of years is somewhat related to ethanol and 

oil markets, but is part of a larger change in 

financial investments. 

An influential 2004 working paper by 

economists at Wharton and Yale (eventually published in Gorton and Rouwenhorst, 

2006) demonstrated that investments in 

commodities could significantly improve 

the risk-reward profile of portfolios previously comprised of only the traditional class 

of liquid assets. That article coincided with 

an increasing belief on the part of large investors that the world economy was headed 

into a period of rapid growth in the value of 

commodities, including agricultural commodities. This confluence of events spurred 

the rapid expansion of investment money 

in commodities. 

 Growing demand for ethanol and 

critical supply shortfalls of wheat, corn, 

and soybeans have combined to drive grain 

prices to record high levels. Accentuating 

this price movement is the impact of speculative investment in commodity markets. 

Commodities have long been viewed as a 

defensive asset class, earning favorable returns in times of inflation at a time when 

stocks and bonds generally decline (Lam, 

2004). But the recent flood of speculative 

money into commodity markets is increasing price volatility and pushing up further 

the prices of raw commodities and food 

products. 

From 1999 to 2007, the average range 

of wheat prices in the July Kansas City 

contract was $1.29 per bushel (life of contract high minus low). Thus far for 2008, 

that range has increased to $8.61 per bushel 

(Figure 8.1). According to Brian Grete, Senior market analyst with Pro Farmer, “Inflationary talk has largely been the reason 

speculative traders have flooded commodity markets with money. While most com-

$3.70

$2.55

$3.60

$2.74

$3.81

$2.89

$3.89

$2.79

$4.08

$2.94

$4.35

$3.13

$4.05

$3.04

$5.23

$3.42

$6.30

$3.80

$13.00

$4.39

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

$2.00

$4.00

$6.00

$8.00

$10.00

$12.00

$14.00

Dollars per Bushel

High

Low

Close

1/3/2007

1/12/20071/24/2007

2/2/2007

2/13/20072/23/2007

3/6/2007

3/15/20073/26/2007

4/4/2007

4/16/20074/25/2007

5/4/2007

5/15/20075/24/2007

6/5/2007

6/14/20076/25/2007

7/5/2007

7/16/20077/25/2007

8/3/2007

8/14/20078/23/2007

9/4/2007

9/13/20079/24/200710/3/2007

10/12/200710/23/2007

11/1/2007

11/12/200711/21/2007

12/3/2007

12/12/200712/21/2007

1/3/2008

1/14/20081/24/2008

2/4/2008

2/13/20082/25/2008

3/5/2008

3/14/2008

1250

1300

1350

1400

1450

1500

1550

1600

S&P 500

300

400

500

600

700

800

GSCI

GSCI S&P 500

Figure 8.1: High, Low, and Close of July KCBT Wheat Prices.

Figure 8.2: Comparison of Returns, GSCI Versus S&P 500 (closing prices).
32

modities (including grains) have bullish 

fundamentals, prices wouldn’t be at current 

levels on fundamentals alone” (2008).

Relatively new investment tools allow 

individuals to join professionals in making 

investments in commodities. Most prominent are index funds dominated by the 

Goldman Sachs Commodity Index (GSCI). 

These funds offer long-only investment in 

a broadly diverse basket of commodities 

that includes energy, industrial metals, precious metals, agricultural commodities, and 

livestock. Annual returns of the Goldman 

Sachs Commodity Index measured in early 

March show a return on investment of over 

50 percent while the return on the Standard 

and Poor’s 500 stock index was a negative 6 

percent (Figure 8.2).

Estimated investment funds in the 

GSCI have increased from $8 billion in 

2000 to over $103 billion today (Cohn and 

Symonds, 2004, Goldman Sachs, 2007, 

Brock, 2008) (Figure 8.3). Long only funds 

such as the GSCI typically hold their positions in the nearby futures contracts. As 

contract expiration nears, they ‘roll’ their 

futures positions into the next nearest 

contract month. The price exaggeration 

generated by these investment funds is 

so pervasive, traders have coined a phrase 

to explain price activity at these critical 

junctures: the Goldman Roll (van Essen, 

2007). 

What About Hedging Opportunities?

The increased activity in futures markets has had the unexpected consequence 

of reducing producer’s ability to manage 

price risk using futures markets. The large 

influx of money into the markets, typically long positions, has pushed commodities to extremely high levels. But, these 

funds also quickly move large amounts of 

money in and out of positions. This has 

generated much more price volatility in 

the futures markets. In response, the ex2000 2004 2007 2008

$0.00

$20.00

$40.00

$60.00

$80.00

$100.00

$120.00

Billion Dollars

Figure 8.3: Investor Funds in the Goldman Sachs Commodity Index.

changes have increased the daily move 

limits for most of the agricultural commodities over the last 6 months. 

The increased volatility has had unintended consequences. Grain elevators, 

large grain companies like Archer Daniels Midland, Bunge, and Cargill, large 

cotton merchants, and cooperatives often 

provide farmers the opportunity to forward contract their crops. At the time the 

forward contract is accepted the elevator or 

merchant normally takes an offsetting position in the futures market to protect their 

offered price and limit their risk. However, 

the up and down volatility in the market 

and expanded trading price limits mean 

that more margin calls occur. Small elevators and even large grain companies and 

cotton merchants, who are trading even 

larger volumes, not to mention farmers doing their own price risk management, have 

been unable to make the margin calls. 

Producers, elevators, and companies 

use bank financing to finance their businesses and the price risk management. As 

the margin calls have increased they have 

exhausted their ability to finance their normal hedging activities and have therefore 

been forced out of the market. The end result is an inability to use the futures market 

to manage price risk due to the increased 

price volatility and a lack of financing to 

bridge the growing season. 

It is unclear, at this time, how this situation can be reversed. Futures market price 

volatility is unlikely to decline unless fund 

activity is reduced. Even without fund activity in the markets given demand and 

supply situations for some commodities, a 

measure of increased price volatility could 

be expected. There are no regulations anticipated, at this time, to limit participation 

in the markets. 
33

9

Estimating 

the 

Variability 

in Corn 

Price

The volatility in corn prices and 

changing market leads to the natural 

question, “how high could corn prices 

go?” Increased demand, tighter stocks, 

and the normal variation in yields caused 

by weather increase the stress on the 

market. This report section addresses 

potential price volatility. 

The past few years’ price action in 

commodities has once again enforced 

the fact that prices can move very far 

and very fast. The AFPC used a simulation model to project possible price and 

yield combinations in 2008 to estimate 

where corn prices might go if there was 

a bumper crop or a severe drought in this 

country. Using a national planting of 86 

million acres of corn with a projected national yield of 153.5 bushels per acre as 

a base point, we accounted for variability in domestic supply by using historical national yields and export numbers. 

Using this data, along with appropriate 

price elasticities and harvested acreage, 

we projected 500 possible outcomes for 

price and yield in 2008 (Figure 9.1).

The model forecasts a national season average mean price of $4.66 per 

bushel in 2008, with a possible range of 

the projected high of $10.05 per bushel 

to a low of $2.68 per bushel. Possible 

national yields have a mean of 153.5 

bushels per acre and range from a high 

of 173.7 bushels per acre to a low of 

115.8 bushels per acre. The figure below shows a graph of the 500 possible 

price and yield combinations. Most of 

the observations are clustered around 

the mean price and yield. The minimum 

0

2

4

6

8

10

12

115 125 135 145 155 165 175

Price per Bushel

Bushels per Acre

Figure 9.1: Scatter Plot of Possible Price and Yield Combinations in 2008.

yield of 115.8 bushels, corresponding to 

the maximum price, represents approximately a 25 percent reduction from the 

projected mean yield. It should be noted 

that a price as high as $10 is very unlikely, as only one of the 500 observation 

(less than one tenth of one percent) of 

the projected prices are greater than $10 

per bushel. In fact, 82% of the projected 

prices are less than $6.00 per bushel, with 

most (53%) of the prices falling between 

$3.50 and $6.00 per bushel. A 10% reduction from the mean yield (drought) 

results in a national average corn price of 

approximately $6.32 per bushel. A 10% 

increase from the mean yield (bumper 

crop) results in a price of approximately 

$3.13 per bushel. Seventy percent of the 

observations fall in the $3.13 to $6.32 

per bushel range. 

Increased demand for corn and 

more corn utilizing infrastructure coupled with the acreage devoted to corn 

means, in economist’s terms, that we 

have moved to a more inelastic portion 

of the demand function. The end result is 

more price variability because small supply changes create larger price changes. 

This analysis suggests an extremely wide 

range of possible price outcomes.
34

10

Economic 

Impacts 

of Higher 

Corn 

Prices on 

the Texas 

Economy

The economic impacts to the grain 

and livestock sectors from higher corn 

and grain sorghum prices are calculated 

using 2007 livestock numbers. Total corn 

and grain sorghum fed to all cattle (beef 

and dairy) in 2007 was about 366.8 million bushels based on the number of fed 

steers sold, cow and bull herd inventory 

January 1, calves marketed, dairy cows, 

and bushels of grain fed per head. Grain 

fed to hogs and poultry in Texas was an 

estimated 25 and 175 million bushels, 

respectively. Feedgrain output was estimated using 2007 production and the 

change in feedgrain price.

The average corn price in the Texas 

Triangle Area increased $2.41/bu between 2005 and January 2008 and grain 

sorghum increased $2.39/bu over the 

same period. The increased market prices 

for corn and grain sorghum in 2007 led 

to increased receipts of $1.1 billion for 

the Texas feedgrain sector (Table 10.1). 

The direct, indirect, and induced effects 

of the higher receipts for corn and grain 

sorghum are summarized in the table 

below. Direct effects represent the initial change in the industry in this case, 

the direct effects resulting from the increase in gross value of corn production. 

Indirect effects result from inter-industry transactions as supplying industries 

respond to increased demands. Induced 

effects reflect changes in local spending 

that result from income changes in the 

directly and indirectly affected industry sectors. The combined indirect and 

induced output effects total about $893 

million and the total effect is to increase 

Table 10.1: Economic Impacts of Higher Grain Prices on Grain and Feeding Sectors in Texas.

Direct Indirect Induced Total

Millions of Dollars

Total Output Effects

 Grains 1,099.8 497.8 395.00 1,992.6

 Cattle (799.5) (811.1) (184.8) (1,795.5)

 Poultry (420.9) (162.7) (104.3) (687.9)

 Swine (60.2) (39.3) (12.4) (111.9)

 Livestock Effects (1,280.6) (1,013.2) (301.5) (2,595.3)

Crop and Livestock (180.8) (515.4) 93.5 (602.6)

Total Value Added Effects

 Grains 110.0 451.3 149.5 710.8

 Cattle (80.0) (328.1) (108.7) (516.7)

 Poultry (42.1) (172.7) (57.2) (272.0)

 Swine (6.0) (24.7) 8.2) (38.9)

 Livestock Effects (128.1) 525.5) (174.1) (827.6)

 Crop and Livestock (18.1) (74.2) (24.6) (116.8) 

economic output for Texas from the corn 

and grain sorghum industries by $1.99 

billion. But, those higher prices for 

feedgrains are paid by livestock producers. The combined total livestock output 

effect is a -$2.59 billion. The combination of feedgrains and livestock results 

in a net reduction in economic output of 

$602 million.

Value added can be thought of as 

similar to the cost of goods sold in an 

accounting sense. It represents the value 

that each sector adds to the product not 

counting the intermediate product purchased. For example, a feedlot adds value 

to the feeder calf they purchased. Each 

industry segment adds some value to the 

whole. The total feedgrain value added 

impact for Texas is $710.8 million. As in 

the case of total output, that value added 

is offset by losses in the livestock sector resulting in net total value added of 

-$116 million.
35

11

Effects of a 

Renewable 

Fuel 

Standard 

Waiver

The initial renewable fuels standard 

(RFS), instituted under the energy bill 

of 2005, always had a limited probability 

of binding or needing to insure the mandated level of ethanol blending given the 

powerful market incentives for ethanol 

production that prevailed in the two 

years following its establishment (Bryant and Outlaw, 2006). There may have 

been some indirect effect, as the initial 

RFS served as a guarantee of minimum 

blending levels and, therefore, production levels affording prospective ethanol 

producers protection from the specter of 

less favorable market conditions. However, industry expansion has been so dramatic that ethanol production volumes 

now would still be at risk of significant 

contraction under the old RFS. This 

dramatic expansion has resulted in feedstock prices being bid up significantly. 

The new RFS, instituted under the 

2007 energy bill, requires significantly 

higher levels of blending. Moreover, 

some flexibility has been removed, vis-avis the old RFS. There is now a specific 

grain-based ethanol (“conventional biofuel”) requirement, whereas previously 

the RFS could be satisfied by the blending of either ethanol or biodiesel (from 

any feedstock). The conventional biofuel 

portion of the new RFS starts at 9 billion 

gallons in 2008 and rises to 13.2 billion 

gallons in 2012 (cf., 5 and 7.5 billion gallons, respectively, under the old, flexible 

RFS). Ethanol production in 2007 was 

approximately 6.5 billion gallons – so 

production of 9 billion gallons in 2008 

in the absence of a government mandate 

cannot be comfortably assumed. A short 

corn crop could very well result in the 

new RFS binding, or insuring ethanol 

production, given the already high levels 

of grain prices and shrunken profitability of ethanol production. 

In this section, we analyze possible 

market outcomes under the new conventional biofuel RFS, and under partial 

waivers of one-quarter and one-half of 

the conventional biofuel RFS. The waivers are assumed to be immediate and 

permanent, although we note that information regarding the conditions under 

which waivers might be granted, and the 

extent to which the RFS might be relaxed, is very limited. The 2007 energy 

bill does not discuss alterations to the 

waiver process for conventional biofuels, 

so it appears that the rules emanating 

from the 2005 energy bill should still 

apply. In broad terms, a waiver could be 

possible if the RFS would “severely harm 

the economy or the environment of a 

State, a region, or the United States,” 

or if there was an inadequate domestic 

supply of renewable fuel. Further guidelines are not forthcoming. EPA states in 

the rules governing RFS administration 

that they had “not finalized regulations 

providing more specificity regarding the 

criteria for a waiver or the ramifications 

of Agency approval of such a waiver 

in terms of the level or applicability of 

the standard,” and that “each situation 

in which a waiver may be granted will 

be unique, and promulgating a list of 

more specific criteria may be counterproductive.” (Environmental Protection 

Agency, 2007). They do, however, state 

unambiguously that any waiver would be 

of national applicability (i.e. there will 

be no waivers that apply only to select 

states or regions), and that waivers may be 

full or partial.

This lack of information regarding the 

nature of any possible waiver makes the 

specification of scenarios for our analysis 

difficult. It appears as though potential petitions for waivers that are currently being 

considered are based on arguments that 

current feedgrain prices are already causing 

economic hardship. It further appears that 

ongoing, rather than temporary, reductions 

in the RFS would be the relief desired by 

potential petitioners. These considerations 

motivate our specifying scenarios that feature immediate and permanent reductions 

in the RFS. 

The effects of a higher RFS were already considered in Bryant (2008, but 

based on analysis conducted in the spring 

of 2007), when various plans for a higher 

RFS were being considered by Congress. 

This report, however, updates that analysis 

in three ways. First, an updated underlying 

agricultural market baseline is employed. 

Second, this analysis reflects recent, substantial increases in crude oil prices. Third, 

the exact specifications of the new RFS 

are used, including the reduced flexibility described above, and some different 

scenarios are considered.

Analysis

The model used for this analysis is 

described in Bryant (2008) and Bryant 
36

NYMEX contracts for Friday, March 28, 

2008, are employed in the fossil energy 

component of the model. 

The high levels of fossil energy prices expected over the next few years result 

in powerful market incentives for ethanol production, in the absence of a supply-related spike in corn prices. Figure 

11.1 illustrates the expected levels (averages across 500 possible future states 

of the world) of national average wholesale market prices for ethanol. Prices 

are likely to remain in the mid-$2.00 

per gallon neighborhood, with expected 

prices being somewhat lower if the RFS 

is relaxed by one-quarter, and somewhat 

lower still (albeit by less than the first 

increment) if the RFS is relaxed by onehalf. These lower expected prices reflect 

the fact that less ethanol production will 

be mandated under poor market conditions that could be realized in some 

years (a short corn crop) that would result in high costs of production, or lower 

demand (low crude oil prices).

Expected levels of ethanol production are illustrated in Figure 11.2. Under 

all scenarios these expected levels are 

above the newly-instituted conventional 

renewable fuels RFS by a billion gallons or more, except for in 2008 when 

the margin is much smaller. This again 

reflects the fact that high fossil energy prices will result in high demand 

for ethanol as a fuel extender (i.e., use 

above the levels required for satisfying 

constraints on oxygen content of blended motor fuel). Partial relaxation of the 

conventional biofuel RFS would result 

2003 2005 2007 2009 2011 2013

1

1.5

2

2.5

3

Dollars per Gallon

Full RFS One-quarter RFS Waiver One-half RFS Waiver History

Figure 11.1: Expected Ethanol Price.

and Outlaw (2006). Very briefly, we employ a hybrid stochastic simulation model with three components. First, random 

future paths of crude oil and natural gas 

prices are generated based on observed 

NYMEX futures and futures options 

prices. Second, possible future agricultural market conditions are reflected by 

the stochastic output of a large-scale 

econometric model of the agricultural 

economy developed and maintained by 

the Food and Agricultural Policy Research Institute. Third, the agricultural 

market equilibria under possible future 

states of the world are displaced based 

on the optimizing behavior of representative biofuels producers, given fossil 

fuels and agricultural market conditions, 

and constraints on production levels due 

to capacity limitations and government 

regulation. The results presented below 

reflect the distributions of market variables over 500 realizations of possible 

future states of the world.

We consider three scenarios. First, 

the current RFS, and all other government programs, proceed under their 

currently planned configurations. In the 

second scenario, the conventional biofuel RFS is immediately and permanently 

reduced by one-quarter. In the third 

scenario, the conventional biofuel RFS 

is reduced by one-half. In all scenarios, 

the tax credits for ethanol and biodiesel 

blending are assumed to persist, and the 

new biodiesel-specific RFS is assumed 

to continue at the 1 billion gallon level 

after 2012.1

 Closing market prices for 

1

 This is a slight deviation from the current specification, wherein the biodiesel RFS rises to 1 billion gallons in 2012, then goes to zero.
37

2008 2009 2010 2011 2012 2013 2014

0.0

0.1

0.2

0.3

0.4

Probability of Binding RFS

Figure 11.3: Probability of a Binding RFS.

in somewhat lower expected levels of 

production, as production would be lower if unfavorable market conditions were 

realized (high corn prices or low crude 

oil prices). 

Model results also provide information regarding the probability of the 

conventional biofuel RFS (at its full level) binding, as depicted in Figure 11.3. 

This probability is fairly low for 2008 

and 2009 (see a caveat below related to 

this result), and gradually increases further into the future. This does not so 

much reflect the rising level of the RFS, 

as expected levels of ethanol production are rising faster than the RFS (see 

Figure 11.2). Rather, this reflects the 

cumulative uncertainty regarding crude 

oil prices as we forecast further into the 

future and the commensurately rising 

uncertainty over market incentives for 

ethanol production.

Results across scenarios with respect to corn prices are not too surprising, given the expected levels of ethanol 

prices described above and uncertainty 

about those expected levels. Like ethanol prices, expected corn prices (Figure 

11.4) are fairly steady near current levels under all scenarios. Expected prices 

across scenarios gradually diverge, with 

the one-quarter RFS waiver price falling about $0.30 per bushel below the 

full RFS price a few years hence, and 

the one-half RFS waiver price falling 

about $0.50 to $0.60 per bushel below 

the full RFS expected price. Again, these 

results reflect the tail events – those possible future states of the world in which 

2003 2005 2007 2009 2011 2013

0

5

10

15

20

Billions of Gallons

Full RFS

One-quarter RFS Waiver

One-half RFS Waiver History Conventional Biofuels RFS

Figure 11.2: Expected Ethanol Production.
38

$4.00 $5.00 $6.00 $7.00 $8.00 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Probability of Exceeding Corn Price

Full RFS One-quarter RFS Waiver One-half RFS Waiver

Figure 11.5: Probabilities of Various Corn Prices.

03/04 05/06 07/08 09/10 11/12 13/14

1

2

3

4

5

6

Dollars per Bushel

Full RFS One-quarter RFS Waiver One-half RFS Waiver History

Figure 11.4: Probability of a Binding RFS.

the RFS would be binding, and the levels of ethanol production that would be 

mandated under those conditions. The 

probabilities of various corn prices being realized under the three different 

scenarios are illustrated in Figure 11.5. 

Corn prices below $4.00 per bushel are 

unlikely under any scenario, given the 

high energy prices expected.

Some caveats bear mention. The 

agricultural baseline used in this analysis reflects a noticeably higher level of 

acres planted with corn in 2008 than recent USDA reports are suggesting (93.6 

versus 86 million acres). Our model 

therefore generates corn price forecasts 

that are lower than might be realized in 

2008. Moreover, corn price uncertainty 

is believed to be understated in the agricultural baseline that is an input for our 

model. A short corn crop in 2008 could 

very well result in substantially higher 

prices in the ‘08/’09 crop year than the 

baseline reflects, particularly given that 

the RFS is likely to be close to binding 

for 2008 (Figure 11.3). A final caveat is 

that the growth in ethanol production 

capacity is exogenously specified. Significantly negative profits for ethanol 

producers, especially in the early years of 

our forecast horizon, would almost certainly result in lower growth in production capacity than we assume and lower 

levels of ethanol production in later 

years, even if favorable market conditions return.
39

12 References

Agricultural & Food Policy Center. Representative 

Farms Economic Outlook for the January 2008 

FAPRI/AFPC Baseline. AFPC Working Paper 

08-1. Texas A&M University, February 2008.

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40

A policy research report presents the final results of a research project undertaken by AFPC faculty. At least a portion of the contents of 

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