United States 

Department of 

Agriculture

Economic

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Scott Malcolm, Elizabeth Marshall, Marcel Aillery, 

Paul Heisey, Michael Livingston, 

and Kelly Day-Rubenstein

Agricultural Adaptation 

to a Changing Climate

Economic and Environmental Implications 

Vary by U.S. Region Economic 

Research 

Report 

Number 136

July 2012
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Recommended citation format for this publication: 

Malcolm, Scott, Elizabeth Marshall, Marcel Aillery, Paul Heisey, Michael 

Livingston, and Kelly Day-Rubenstein. Agricultural Adaptation to a Changing 

Climate: Economic and Environmental Implications Vary by U.S. Region, 

ERR-136, U.S. Department of Agriculture, Economic Research Service, 

July 2012.

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United States

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A Report from the Economic Research Service

Abstract

Global climate models predict increases over time in average temperature worldwide, 

with signifi cant impacts on local patterns of temperature and precipitation. The extent to 

which such changes present a risk to food supplies, farmer livelihoods, and rural communities depends in part on the direction, magnitude, and rate of such changes, but equally 

importantly on the ability of the agricultural sector to adapt to changing patterns of 

yield and productivity, production cost, and resource availability. Study fi ndings suggest 

that, while impacts are highly sensitive to uncertain climate projections, farmers have 

considerable fl exibility to adapt to changes in local weather, resource conditions, and 

price signals by adjusting crops, rotations, and production practices. Such adaptation, 

using existing crop production technologies, can partially mitigate the impacts of climate 

change on national agricultural markets. Adaptive redistribution of production, however, 

may have signifi cant implications for both regional land use and environmental quality.

Keywords: climate change, adaptation, water resources, agricultural pests, Regional 

Environment and Agriculture Programming (REAP) model, regional crop mix, regional 

environmental effects, drought tolerance, pest management

Acknowledgments

The authors gratefully acknowledge the assistance of Gerald Nelson from the International 

Food Policy Research Institute and USDA Economic Research Service analysts Ryan 

Williams, Vince Breneman, and Paul Westcott for their contributions. The authors appreciate the thoughtful reviews from Allan Auclair, Noel Gollehon, Jerry Hatfi eld, Greg 

Johnson, Ron Sands, Roger Sedjo, along with Bill Hohenstein and the staff of USDA’s 

Climate Change Program Offi ce. The authors also thank Dale Simms, Angela Anderson, 

and Wynnice Pointer-Napper for editorial and design assistance.

Agricultural Adaptation 

to a Changing Climate

Economic and Environmental Implications 

Vary by U.S. Region

Scott Malcolm, smalcolm@ers.usda.gov

Elizabeth Marshall, Marcel Aillery, Paul Heisey, 

Michael Livingston, and Kelly Day-Rubenstein

Economic 

Research 

Report 

Number 136

July 2012
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Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 Scope of the Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Climate Change and Agricultural Impact Analysis . . . . . . . . . . . . . . . . . 8

 Regional Environment and Agriculture Programming Model . . . . . . . . . 8

 Characterizing the Study’s Climate Projections . . . . . . . . . . . . . . . . . . . 10

 Quantifying Climate Change Impacts on Crop Yields . . . . . . . . . . . . . 12

 Quantifying the Impacts of Climate Change and Adaptation 

 Behavior Using REAP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

 Quantifying the Additional Impacts of Pest Distribution Change . . . . . 15

 Quantifying the Impacts of Drought-Tolerant Varieties . . . . . . . . . . . . . 17

Quantifying the Agricultural Impact of Climate Change 

 and the Potential for Agricultural Adaptation . . . . . . . . . . . . . . . . . . 18

 Climate Impact Analysis: No Adaptation . . . . . . . . . . . . . . . . . . . . . . . . 18

 Climate Change Impacts When Farmers Adapt Crop Rotations, 

 Tillage, and Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

 How Farmer Adaptation Affects Measured Impacts 

 of Climate Change. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

 Impact of Changes in Pest Prevalence on Crop Production 

 Under Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Lessening the Impact of Climate Change Through Technical Change: 

Targeting Crop Breeding for Adaptation to Climate Stress . . . . . . . . . 41

 Adoption of Plant Varieties With Greater Tolerance 

 to Climate Change-Related Stresses . . . . . . . . . . . . . . . . . . . . . . . . . . 41

 Impact of Adopting Drought-Tolerant Crop Varieties. . . . . . . . . . . . . . . 44

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

 Analysis Limitations and Future Research . . . . . . . . . . . . . . . . . . . . . . . 55

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendix A: The REAP Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Appendix B: Climate Projections for 2030 . . . . . . . . . . . . . . . . . . . . . . . 68

Appendix C: Sensitivity of Analysis Results to Climate 

 Change Elements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Appendix D: An Econometric Model of Regional 

 Pesticide Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
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Summary

What Is the Issue?

Agricultural production has always been affected by variability in weather, 

and U.S. farmers have adopted production practices and strategies appropriate 

to their local climate. The weather that shapes the structure of U.S. agricultural production, however, is changing along with world climatic conditions. Climate models predict increases in average temperatures worldwide, 

with wide-ranging impacts on local temperature and rainfall. Whether such 

changes present a risk to food supplies, farmer livelihoods, and rural communities depends partly on the direction, magnitude, and rate of such changes, 

but also on the agricultural sector’s responsiveness to changing yield and 

productivity patterns, production costs, and resource availability. Adaptive 

behaviors will allow producers to mitigate costs of climate change and even 

to capitalize on new opportunities. The introduction of crop varieties better 

adapted to new growing conditions could facilitate this transition. 

What Did the Study Find?

The projected impacts of climate change in 2030 vary widely both across 

climate scenarios and across regions within a single scenario, primarily due 

to the direction and magnitude of precipitation changes. Farmers’ ability to 

alter crops, rotations, and production practices enables them to lessen the 

impact of changes in local weather, resource conditions, and price signals. 

Redistributing production across regions can greatly mitigate the impact 

of climate change on national agricultural markets. Such redistribution, 

however, will alter land use and environmental quality. Key fi ndings (with 

ranges expressed across different climate scenarios) include:

• National acreage changes when farmers adapt are relatively small across 

climate change scenarios (from 0.2 to 1.0 percent compared with the 

baseline), although acreage changes vary considerably by region. Crop 

acreage and planting patterns in the Corn Belt and Northern regions, in 

general, are less sensitive to climate change than in Southern regions, 

where yield changes have a wider range across crops (for example, 

acreage changes in the Delta region range from -9.8 to 5.0 percent). 

Acreage changes indicate considerable capacity in the agricultural system 

to reallocate crop production in response to shifting conditions. 

• Although climate change leads to higher prices for corn and soybeans 

under hotter, drier scenarios as a result of considerably lower national 

yields, adaptation to climate change dampens the rise in prices for most 

commodities.

• Aggregate national returns to crop production decline with the increasing 

severity of the climate change scenario. The same trend holds for the 

Corn Belt, which accounts for over half of all returns to U.S. fi eld crop 

production. The complex interaction between regional yield changes, 

markets, and production options—combined with the Corn Belt’s large 

production—creates a larger absolute impact than in other regions, 

although the percentage decline in returns is smaller than in other 

regions. Changes in returns vary in the other regions, however, with no 

direct correspondence to the magnitude of the scenario’s temperature and 
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precipitation change. This is due to shifts in the economic attractiveness 

of crops in regions other than the Corn Belt.

• Aggregate impacts of climate change on net returns to crop farmers range 

from an estimated increase of $3.6 billion to a loss of $1.5 billion per year, 

under the four climate change scenarios. Spread and redistribution of agricultural pests may reduce these returns by $1.5 billion to $3.0 billion.

• Regionally, crop sector impacts from climate change are likely to be 

greatest in the Corn Belt, with annual losses ranging from $1.1 billion to 

$4.1 billion across scenarios. Heightened damage from crop pests could 

lead to additional losses of $400 million to $600 million in that region. 

Economic effects in other regions may be positive or negative, depending 

on how well crop rotation and tillage practices accommodate changes in 

temperature and precipitation and how market-mediated prices change 

for predominant regional crops. Drought-tolerant varieties increase returns 

nationally and in regions that plant them, indicating that further development of drought-tolerant varieties could be benefi cial under a wide range of 

adverse climate changes.

• Changes in crop production result in and refl ect changes in crop prices. 

Soybean markets may be particularly sensitive, with estimated price 

effects ranging from -4 to 22 percent. Corn prices are estimated to change 

between -2 and 6 percent, while wheat prices are estimated to decline 

across all four scenarios. Shifting agricultural pest populations cause 

the price range to widen and crop prices to increase for all crops except 

cotton. The availability of drought-tolerant crop varieties is estimated to 

reduce prices. 

• Climate change is projected to slightly increase aggregate natural 

resource and environmental impacts from U.S. agricultural production, 

although local effects may be more signifi cant. Cropland area is projected 

to expand 0.2-1.0 percent, while nitrogen fertilizer losses are projected 

to grow 1.4-5.0 percent. Rainfall-related soil erosion changes range from 

-0.9 to 1.2 percent above baseline levels. The disproportionate change in 

nitrogen loss to water relative to acreage expansion refl ects changes in 

regional crop distribution, input use, and the varying impacts of changes 

in production practices. 

This report focuses on how crop farmers will adapt to changing climate 

conditions and how extensively changing pest pressures and emergent technologies such as drought-resistant crops might alter the benefi ts of adaptation. 

While interactions between the crop and livestock sectors are included in 

the analysis, changes in the livestock sector are not the focus of the report. 

Consumers will likewise be affected by adjustments in both the crop and livestock sectors. Livestock producers will see changes in the prices they pay for 

feed, and retail food prices will adjust to commodity price changes. 

Our climate change analysis focused on the yield-related impacts associated 

with increased average temperatures, regional changes in average precipitation, increased carbon dioxide concentration in the atmosphere, the expanded 

incidence of pests, and the market-mediated price impacts that arise from 

regional shifts in crops and practices. Model limitations precluded analysis of 

yield impacts from the potential increase in extreme weather events, nor could 
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the analysis address the potential for, and constraints to, expanding irrigated 

acreage and water use, which is particularly important in the Western United 

States where there is already signifi cant competition for water resources.

How Was the Study Conducted?

Downscaled climate projections from four different general circulation models—

based on the Intergovernmental Panel on Climate Change’s (IPCC) Special Report 

on Emissions Scenarios (SRES) A1B emissions scenario—represent possible 

climate futures in the United States. A crop-growth simulator—the Environmental 

Productivity and Integrated Climate (EPIC) model—is used to estimate the 

effect on crop yields of associated weather patterns resulting from each climate 

projection and a suite of environmental indicators associated with each regional 

production enterprise, which consists of a single crop rotation/tillage/fertilizer 

regime. Climate projections, historical climate data, and Agricultural Resource 

Management Survey (ARMS) data are also used to estimate cost and yield 

impacts associated with potential changes in the geographic distribution and 

severity of pest and disease outbreaks resulting from climate change. The Regional 

Environment and Agriculture Programming (REAP) model—a mathematical 

programming model of the U.S. agricultural sector—is then used to project shifts 

in regional agricultural production given climate-induced changes in crop productivity patterns and price/demand feedback from national commodity and livestock 

markets. REAP also allows researchers to estimate the impact on national agricultural production, crop prices, regional farmer income, and—in combination with 

EPIC results—regional indicators of environmental quality.
.
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Introduction

Agricultural production has always been closely linked with, and vulnerable to, 

trends in weather. As a result, agricultural production enterprises and practices 

have adapted to local climatic conditions, and farmers have developed strategies 

for responding to local weather variability. Corn farmers in the Corn Belt push 

back planting dates in response to a wet spring, for example, and may switch to 

soybean production if persistent wet weather delays corn planting excessively. 

During extremely dry periods, farmers in the Plains States may increase moisture-conserving tillage practices, such as no-till, ridge-till, and mulch-till (Ding, 

2009). Local strategies for weather adaptation are based on years of producer 

experience and farming-system research specifi c to regional conditions.

The range of local weather conditions that has shaped the current structure 

of domestic agricultural production, however, is changing in response to 

broad shifts in general climatic conditions across the country and around the 

world. General climatic conditions have adjusted slowly throughout the 20th 

century, with global average temperature increasing 1.3 degrees Fahrenheit 

(°F) (IPCC, 2007). As atmospheric concentrations of carbon dioxide (CO2) 

have increased, the rate of temperature increase appears to be accelerating, 

and recent climate models predict further warming trends over time that may 

have a signifi cant impact on local temperature and precipitation patterns.

Agricultural productivity, and the degree to which other inputs (such as fertilizer, pesticides, and irrigation) are needed to augment production, depend 

a great deal on local climate conditions. Increases in average temperature, 

changes in precipitation patterns, and increases in the frequency of extreme 

weather events would signifi cantly alter the local production environment 

through the distribution of crop yields, crop acreage planted to different crops, 

reliance on dryland and irrigated production systems, and the geographic range 

and severity of pest outbreaks. Changes in water availability for crop production will be an important factor affecting regional agricultural production. 

Shifting precipitation patterns in combination with warming temperatures may 

increase water scarcity in some regions, intensifying competition for water 

currently used in agriculture. In other areas, increased soil-moisture availability 

may increase opportunities for agricultural production.

Agricultural systems respond to the changing production environment 

associated with climate change through the process of adaptation. The 

Intergovernmental Panel on Climate Change (IPCC) defi nes adaptation as the 

“adjustment in natural or human systems in response to actual or expected 

climatic stimuli or their effects, which moderates harm or exploits benefi cial 

opportunities” (IPCC, 2007). Agricultural systems adapt to climate change 

at a number of levels, from national-level investments in agricultural research 

and development, climate forecasting, or infrastructure to behavioral adjustments of individual farm households. Smit and Skinner (2002) organize agricultural adaptation options within four interdependent categories (table 1): 

• Technological developments, 

• Government programs and insurance, 

• Farm production practices, and 

• Farm fi nancial management. 
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Table 1

Types and examples of agricultural adaptation options

Technological developments

Crop development:

• Develop new crop varieties, including hybrids, to increase the tolerance of and suitability of plants to temperature, 

moisture, and other relevant climatic conditions.

Weather and climate information systems:

• Develop early warning systems that provide daily weather predictions and seasonal forecasts.

Resource management innovations:

• Develop water management innovations, including irrigation, to address the risk of moisture defi ciencies and the 

increasing frequency of droughts.

• Develop farm-level resource management innovations to address the risk associated with changing temperature, 

moisture, and other relevant climatic conditions.

Government programs and insurance

Agricultural subsidy and support programs:

• Modify crop insurance programs to infl uence farm-level risk management strategies with respect to climate-related loss of 

crop yields.

• Modify subsidy, support, and incentive programs to infl uence farm-level production practices and fi nancial management.

Private insurance:

• Develop private insurance to reduce climate-related risks to farm-level production, infrastructure, and income.

Resource management programs:

• Develop and implement policies and programs to infl uence farm-level land and water resource use and management 

practices in light of changing climate conditions.

Farm production practices

Farm production:

• Diversify crop and livestock types and varieties to address environmental variations and economic risks associated with 

climate change.

• Change production intensity to address environmental variations and economic risks associated with climate change.

Land use:

• Use alternative fallow and tillage practices to address climate-related moisture and nutrient defi ciencies.

Irrigation:

• Implement irrigation practices to address the moisture defi ciencies associated with climate change and reduce the risk of 

income loss due to recurring drought.

Timing of operations:

• Change farm operation timing to address the changing duration of growing seasons and associated changes in 

temperature and moisture.

Farm fi nancial management

Crop insurance:

• Purchase crop insurance to reduce the risks of climate-related income loss.

Crop shares and futures:

• Invest in crop shares and futures to reduce the risks of climate-related income loss.

Household income:

• Diversify household income to address the risk of climate-related income loss.

Source: Adapted from Smit and Skinner, 2002
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While adaptation may take many forms throughout the farm economy, this 

report focuses specifi cally on the potential for adaptation at the fi rst level of 

response—farmer behavior. Adapting to changing conditions is nothing new 

for farmers; they regularly adapt to changes in crop demand, new technological developments, farm policy provisions, land development pressure, and, 

most signifi cantly, weather variability. 

Changes in individual farmer behavior in response to climate change may 

include, but are not limited to, growing different crops or crop varieties; 

adjusting planting and harvest dates; altering input use, such as applied fertilizers, pesticides, and water; adopting new production methods; expanding 

planted acreage; or abandoning farming altogether. How individual farmers 

respond to changing conditions is a function of each farmer’s location, 

resource endowment, economic incentives, and knowledge of alternatives. 

While farming enterprises are likely to adapt in some way to shifting climate 

conditions, the costs and benefi ts of adaptation may vary considerably 

depending on the farm’s location, the crops grown, and other factors that 

differ across operations. 

Similarly, regional impacts of changing climate will not be homogeneous; 

some regions may see an improvement in crop growth potential, while 

others may face declining productivity (see box, “Climate Change Impact on 

Yields”). An analysis that focuses exclusively on the average effect of climate 

change on national production, commodity and food prices, and agricultural 

trade would mask important regional differences. Climate change that alters 

the relative profi tability of regional crop production may redistribute production and resource allocations across regions, with potentially signifi cant 

implications for producer income, resource use, and environmental quality. 

Assessing the potential impact of climate change on the U.S. agricultural 

sector requires the ability to differentiate among regional impacts and allow 

for adaptive behavior that results in shifts within and across production 

regions in response to changing climate regimes.

Historically, genetic enhancement—the combination of biological research, 

plant breeding, and genetic resources—has played a key role in maintaining 

and improving agricultural productivity. As agriculture adapts to global 

climate change, however, genetic combinations that are optimal for current

growing environments are unlikely to be optimal for future growing environments. Adaptive genetic enhancement of traits, such as drought and heat 

tolerance, may offer critical assistance to producers’ long-term response to 

the challenges of climate change. 

The extent to which changing weather patterns will impact the distribution 

and severity of pests (Hatfi eld et al., 2008) and invasive species (USDA, 

2010a) may also infl uence adaptive decisionmaking strategies in the short 

and long term. Increased pesticide and herbicide use is one possible response 

(Bridges, 1992; Joyce et al., 2008). Genetic manipulation of crops to better 

resist pest and disease infestation is another. Crop distribution may also 

change, with production of vulnerable crops moving to less risky regions. 

Fully characterizing the potential agricultural impact of climate change 

means assessing how crop distributions, yield impacts, and the costs of 

prevention and control might be affected by regional temperature changes 

and precipitation levels and associated shifts in agroecological systems.
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There are several pathways through which the changing 

conditions associated with climate change are likely to 

infl uence crop growth and development. 

Increasing Temperatures

The impact of increasing temperatures on crop growth will 

depend on how climate change shifts local temperatures 

relative to the optimal temperature range for the crop varieties 

growing in that region. Research suggests that crops may be 

particularly sensitive to temperature extremes during the 

reproductive phase, when pollen viability and seed setting 

are vulnerable to high temperatures (USCCSP, 2008). 

Higher average temperatures may also result in accelerated 

crop maturity, as optimal air temperatures for growth occur 

earlier in the season, which can result in less seasonal growth 

and lower yield potential. 

Temperature also has an important effect on crop water 

demand. Increased crop water requirements under a warming 

climate may place greater demands on available soil 

moisture and irrigation water supplies. Actual water demand 

will depend on other climatic factors as well, including fi eld 

humidity and shifts in solar radiation caused by changing 

cloud cover and aerosol concentrations. 

Changes in Local Precipitation Patterns

A signifi cant body of research has addressed the impact of 

climate change on water resources (NWAG, 2000; Thomson 

et al., 2005; IPCC, 2007; USCCSP, 2008; USDOI, 2011). 

While General Circulation Models (GCMs) predict a wide 

range of future precipitation patterns for the United States, 

some projected precipitation trends have emerged more 

consistently than others from the modeling literature. 

Annual precipitation has been projected to increase over 

much of the Eastern United States and across the middle-tohigh latitudes of the Central and Western United States.1 In 

contrast, potential precipitation declines are projected for the 

Southwest, Central Mountain region, Southern Plains, and 

Delta region, with the direction of precipitation change less 

evident across the Southeastern United States and Central 

Plains. Changes in total precipitation are also projected to 

be accompanied by interseasonal shifts in the timing of 

precipitation, with a larger share of precipitation falling in 

the winter months and smaller amounts in the summer. 

Increasing Atmospheric Carbon Dioxide (CO2)

Crop yields have been observed to increase with increasing 

levels of atmospheric CO2, though yield response differs by 

crop. Yield increases associated with increasing CO2 arise 

through two pathways: increased rates of photosynthesis 

and reduced water loss through transpiration. Research 

suggests that rising CO2 concentrations that limit plant 

transpiration through the stomata could help mitigate the 

increase in crop water stress experienced as a result of higher 

temperatures (Izaurralde et al., 2003). The transpiration effect 

(the magnitude of which depends largely on soil moisture 

levels) operates in all crops. Impacts on crop yields via the 

photosynthetic pathway, however, operate only in a subset 

of plants. Plants have two different metabolic pathways for 

photosynthesis—C3 and C4—but only the C3 photosynthetic 

pathway responds to increased atmospheric CO2. C3 crops are 

therefore projected to have a higher yield response to increased 

atmospheric CO2 than are C4 crops. 

Among the REAP model’s major fi eld crops, only corn and 

sorghum are C4 plants; other major crops, such as wheat, 

soybeans, and cotton, are classifi ed as C3 crops and therefore 

are more likely to respond positively to increased atmospheric 

CO2.

2 The U.S. Climate Change Science Program (USCCSP, 

2008) reported that a doubling of CO2 increased estimated 

yields by approximately 4 percent for corn, 0-8 percent 

for sorghum, 44 percent for cotton, and 34-38 percent for 

soybeans. Actual responses to increasing atmospheric CO2

will depend upon whether crop growth is constrained by 

other stressors, such as nitrogen or water limitations.

Changing Patterns of Pests and Disease

Changes in the geographic distribution of crop pests and 

diseases (Hatfi eld et al., 2008) and invasive species (USDA, 

2010a; Mooney and Hobbs, 2000; Ziska et al., 2010) as 

a result of climate change are expected to increase yield 

losses and management costs. In general, weed species are 

expected to benefi t more than crop species from increasing 

temperatures and CO2 concentration levels, and crop species 

less able to adapt to changing climatic conditions are expected 

to be more susceptible to attack by pests. Although the shift in 

range of particular pests and invasive species will vary, with 

some expanding and others contracting (Bradley et al., 2009), 

climate change is expected to lead to a northward expansion

Climate Change Impacts on Crop Yields

1These projections are generally consistent with higher levels of recorded precipitation over the latter half of the 20th century 

(USCCSP, 2008).

2The quantitative impacts of increased CO2 on yield are still being discussed in the literature. Most results come from greenhouse or 

open-top chambers in the fi eld, with only a few experiments conducted through FACE (free-air carbon dioxide enrichment) methods, 

which may provide results more representative of actual fi eld conditions (Lobell and Burke, 2010).

Continued on page 5
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Individual farmer decisions, when aggregated to the national level, will have 

consequences on agricultural markets through production levels, trade, and 

prices and on resource use that affects environmental quality. Production 

adjustments may involve changes in aggregate land under cultivation; regional 

cropping pattern shifts, including movement of crops into areas not historically cultivated; changes in the distribution of regional crop rotations; and 

changes in tillage practices and fertilizer use. This study explores the regional 

and national implications of such farm-level adjustments for agricultural 

markets and environmental quality and the net effect of such adjustments on 

the projected impact of climate change on U.S. crop production.

Scope of the Research

Our research focused on how the crop sector might respond to climate 

change, specifi cally: 

• How might farmers adjust land-use and land-management decisions when 

faced with a new production regime shaped by climate change, and what 

are the implications for regional expansion and contraction of cropland?

• How might negative impacts, such as higher prices to consumers, lower 

incomes for farmers, and intensifi cation of environmental consequences, 

be reduced or eliminated through adaptation to climate change? 

• How might changes in climate and the geographic distribution and 

severity of pest and disease outbreaks affect crop production and prices 

for major U.S. fi eld crops? 

of many damaging pests and diseases. Furthermore, herbicide 

use and associated costs are expected to increase, not only 

because of increases in pest pressure, but also because 

herbicides generally become less effective as temperatures 

and CO2 levels rise (Kiely et al., 2004). Control costs and 

crop losses as a result of weeds, insects, and diseases are 

therefore expected to increase, especially in northern regions 

where U.S. fi eld crop production is concentrated.

Changes in Soil Fertility and Erosion Rates

For many years, researchers have speculated that the 

higher temperatures associated with climate change could 

accelerate the decomposition of organic matter in the soil, 

making soil less fertile and quicker to release CO2 and nitrous 

oxide (N2O) from the soil. As far back as 1938, scientists 

recognized the importance of soil organic matter to maintain 

soil productivity and access to plant-available nitrogen and 

observed an empirical relationship between decreased soil 

organic matter and increased temperature and/or decreased 

precipitation (Albrecht, 1938). Recent fi eld research has 

corroborated that differential impacts of temperature on soil 

organic compounds may mean that warmer temperatures 

shift soil molecular carbon composition toward forms of 

carbon less accessible to plants (Feng et al., 2008). Increased 

soil erosion may also contribute to soil fertility losses in a 

warming world. Climate change may impact erosion rates 

through a number of possible pathways, including increased 

intensity of rainfall events, shifting incidence of precipitation 

from snowfall to rainfall, changing soil organic structure, 

and changes in residue or litter cover due to changing yields, 

cultivation practices, and decomposition rates. 

Changes in Climatic Variability 

and the Incidence of Extreme Events

Climate modeling assessments also point to an increase 

in precipitation variability, including increases in extreme 

weather events, as a potential result of a warming climate. 

Risk of fl ood damages are likely to rise in basins projected 

for higher annual runoff or rapid early-season snowmelt 

runoff (USDOI, 2011). Greater frequency and intensity 

of storm events would likely increase the potential for 

pollutant runoff. Evidence also points to increasing drought 

frequency and severity, particularly across the central and 

southern tier regions of the United States (USCCSP, 2008; 

Strzepek et al., 2010).

Continued from page 4
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• What impact might advances in crop research and development have on 

the farm production environment?

• How might the response of crop production to climate change impact soil 

and water quality?

We explored climate change’s impacts on crop production and the potential for, and possible constraints to, adaptive behavior that addresses those 

impacts. Our quantitative modeling analysis then empirically examined the 

implications of altered climate regimes on production patterns and market 

conditions projected to occur by the year 2030. The quantitative methodology 

consisted of three phases: 

• PHASE I: The Environmental Productivity and Integrated Climate 

(EPIC) model linked projections of future climate conditions to crop 

yields and other biophysical indicators. Since future climate projections 

are highly uncertain, we employed climate projections from several 

models to capture a range of possible climate outcomes. 

• PHASE II: The Regional Environment and Agriculture Programming 

(REAP) model was fi rst used to examine regional crop and livestock 

production, input use, cropping practices, economic returns, and environmental quality based on projected USDA market and production conditions under constant climate conditions (i.e., no climate change). REAP 

was then used to examine how production, market, and environmental 

measures behave under the climate change scenarios. To isolate the effect 

of adaptation, we examined two initial cases assuming climate change: 

one where farmers are not allowed to adapt by adjusting crop acreage 

or production practices, and a second where farmers can choose crop 

acreage, rotation, and tillage in response to climate-induced changes in 

crop yields.

• PHASE III: We refi ned the adaptation case explored in phase two to 

consider two important issues that may affect behavioral outcomes 

under climate change. One case estimated possible changes in costs and 

yields due to a shift in the geographic distribution and severity of pest 

outbreaks. A second case introduced changes to yields that might result 

from research and development supporting crop genetic resources for 

drought tolerance.

This research focused on agricultural production in the United States. Our 

purpose was to illustrate possible regional impacts of climate change within 

the United States and to explore the potential for existing and proposed production technologies both to mitigate negative impacts and to take advantage 

of benefi cial impacts of regional shifts in relative crop yield. We recognize, 

however, that ecosystems across the globe will be affected by climate change, 

putting additional pressure on international markets and agricultural production 

systems worldwide through changes in commodity demand, trade patterns, and 

broader economic conditions. This study does not explicitly incorporate international market changes caused by climate change into its analysis. 

While REAP’s strength lies in the specifi cation of crop production detail for 

major commodity crops, the model’s structure also allows for a limited set of 

adaptation behaviors within the livestock sector. The model permits livestock 
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producers to change what they feed livestock in order to minimize costs 

under the new price regimes associated with climate change. The fl exibility 

of such changes, however, is limited to a pre-existing spectrum of historically observed diet options. New grains, feed meals, and feed combinations 

that historically have not been used are not included in the set of livestock 

diet options. Furthermore, the impacts of climate change on the livestock 

sector are limited to those experienced indirectly through feed markets and 

increased competition for pasture land; the model does not capture the direct 

impacts of climate change on livestock productivity and production costs, 

such as those associated with climate control costs for confi ned livestock 

production (Key and Sneeringer, 2011). While livestock supply and demand is 

included in the model, results for the sector are not reported.

We fi rst established a baseline scenario that assumes the current climate will 

prevail through 2030. The “no climate change scenario” establishes a baseline pattern of rotation acreages that meets a projected set of yield, production, price, and acreage measures under a set of “current” weather conditions, 

as measured by an average of weather conditions between 1950 and 2000. 

To measure the impact of climate change on those production patterns, we 

considered four analytical cases that refl ect differing scopes of potential 

climate change impact and behavioral response, as shown in table 2. These 

cases will be described more fully as they are introduced in the report. While 

not exhaustive, the cases illustrate the implications of different elements of 

climate change impact and potential opportunities for adaptation across a 

range of climate change scenarios. 

Table 2

Description of analysis cases

Cases: U.S. agriculture in 2030

Climate 

change 

scenarios

Adaptation 

allowed

Additional 

pest 

effects

Droughttolerant 

varieties 

available

Baseline: Without climate change but with anticipated changes in export 

demand1 for agricultural commodities and continued historical rates of growth 

in U.S. crop yields. No - - -

No adaptation: With direct effects of climate change in temperature and precipitation but no farmer adaptation in crops, rotations, tillage, or land use. Yes No No No

Farmer adaptation: With direct effects of climate change but farmers adapt 

through changes in crops, rotations, tillage, and land use using existing technical options. Yes Yes No No

Additional pest damage: In addition to direct effects, climate change also 

alters the distribution of agricultural crop pests, leading to reduced yields and 

increased pesticide use and management costs. Yes Yes Yes No

Drought-tolerant varieties: Assumes farmers may adopt new, droughttolerant varieties for some crops. Yes Yes No Yes

Note: Adaptation refers to changes by farmers in crops, crop rotations, tillage, and land use in response to the direct effects of climate change 

on temperature and precipitation.

1The trend in projected future demand for U.S. farm exports does not vary across the cases, or with climate change. Although climate change 

may also affect foreign demand for U.S. agricultural commodities, modeling these effects is beyond the scope of this report.
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Climate Change and Agricultural 

Impact Analysis

Regional Environment and Agriculture 

Programming Model 

The Regional Environment and Agriculture Programming (REAP) model is a 

mathematical optimization model that quantifi es agricultural production and 

its associated environmental outcomes for 48 “REAP” regions as defi ned by 

the intersection of USDA Farm Production Regions (defi ned by State boundaries) and Natural Resources Conservation Service (NRCS) Land Resource 

Regions (defi ned by predominant soil type and geography) shown in fi gure 1. 

REAP solves for regional acreage and production levels for 10 crops and 13 

livestock categories and national production levels for 20 processing sectors 

that rely on crop and livestock inputs. REAP explicitly models regional 

differences in crop rotations, tillage practices, and input use, such as fertilizer 

and pesticides. Although crop patterns are determined at the REAP region 

level, results are aggregated to USDA’s Farm Production Regions (bold lines 

in fi g. 1) for presentation in this study. 
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Each REAP model region includes a set of available crop rotations that are 

implemented using one of up to fi ve tillage practices. The combination of 

region, rotation, and tillage practice is referred to as a production enterprise 

and represents the basic unit of crop production economic activity in the 

REAP model. A selection of regionally appropriate production enterprises 

was derived for each REAP region from 1997 National Resources Inventory 

(NRI) data. When REAP solves for agricultural production patterns under 

changed climate, technology, or policy conditions, acreage in each region is 

distributed among production enterprises based on an assessment of relative 

rates of return arising from differences in yields, costs, and returns and is 

further constrained by acreage distribution parameters that capture historically observed patterns of production. (For more information on the REAP 

modeling framework, see Appendix A.) 

REAP Baseline

To construct a baseline against which to compare the impacts of climate 

change, REAP’s pattern of production enterprises was calibrated to projected 

agricultural production conditions for 2030, assuming constant climate conditions based on climate variables calculated as a monthly average over 1950-

2000 (Hijmans et al., 2005). This “no climate change” baseline scenario 

assumes that technology and market conditions will continue to change at 

historical rates and holds the suitability of a given region to produce crops 

constant according to a baseline set of weather conditions.

Technology and market condition projections in 2030 were extrapolated 

from USDA’s annual agricultural production and market indicator projections (USDA, 2010b). The USDA projections include estimates of planted 

and harvested acreage, anticipated crop yields, trade volumes, and market 

prices to 2030 (table 3). The projections assume that agricultural policies 

remain constant and that improvements in crop yields grow at a fi xed rate. 

Yield estimates for the baseline case were calculated by fi rst running EPIC 

under the “current” weather conditions. EPIC yields were then adjusted, using 

Table 3

Baseline production and market projections for 2030

Crop

Planted 

acres

(million)

Harvested 

acres

(million)

Production

(million bushels, 

except as 

noted)

Harvested 

yield

(bu/acre)

Price 

(dollars)

Corn 89 81.8 16,400 200.4 3.65

Sorghum 6.7 5.8 370 63.4 3.45

Barley 3.3 2.9 252 78.2 3.93

Oats 3.1 1.3 192 71.5 2.25

Wheat 52.5 44.6 2,371 50 4.7

Rice1 3.075 3.057 296.9 84.35 16.76

Soybeans 76 75 3,829 51 9.3

Cotton2 11.5 10.2 21.495 2.125 333.6

1Rice units measured in million cwt, or hundredweight. 

2Cotton units measured in million 480-pound bales.

Source: USDA, Economic Research Service, Paul Westcott, personal communication, 2011.
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a crop-specifi c adjustment factor, to meet projected average yields for 2030 

to capture assumptions about exogenous increases in crop productivity. The 

baseline projections do not consider the likelihood of shocks to agricultural 

production from extreme weather or changes over time in other economic 

conditions, such as energy prices, incomes, or exchange rates that might 

affect exports, imports, or input prices. 

Characterizing the Study’s Climate Projections

There are various sources of uncertainty associated with generating estimates 

of future local weather conditions suitable for agricultural production impact 

analysis. Most signifi cant among these are:

• The rate at which carbon and other greenhouse gases (GHG) are expected 

to be emitted into the atmosphere in the coming decades;

• The effects of that GHG accumulation on climate dynamics and core 

average climate variables, such as temperature, precipitation, and relative 

humidity; and

• Our capacity to downscale geographically coarse average climate projections 

into local projections for temperature, precipitation, and other variables that 

refl ect daily weather changes across a fi ner spatial and temporal scale.

The IPCC Special Report on Emissions Scenarios (SRES) addressed the 

fi rst source of uncertainty. The report defi ned several emissions scenarios 

that refl ect different sets of assumptions about global population change, 

technology adoption, energy use, and macroeconomic conditions. The uncertainty surrounding carbon dynamics and climate response is refl ected by 

a wide array of models that attempt to project future climate trends. These 

models—general circulation models (GCMs)—differ from one another in the 

numerical methods used, as well as in the spatial resolution at which climate 

projections are made. As a result, different models may vary considerably 

with respect to predictions of the magnitude and direction of precipitation 

and temperature change for given points or regions.

Each of the IPCC scenarios represented an estimated future path of 

CO2 emissions that can be used as emissions input data into a GCM. 

Atmospheric CO2 levels are an important driver of many long-term climate 

phenomena, so the various emissions scenarios result in different long-term 

climate projections. This analysis used climate projections derived from a 

single emissions scenario—the SRES A1B emissions scenario—which was 

designed to refl ect “very rapid economic growth,” “the rapid introduction 

of new and more effi cient technologies,” and a balanced portfolio of energy 

sources that included both fossil fuels and renewable energy technologies 

(IPCC, 2007). The SRES A1B emissions scenario represented a middle 

ground between other illustrative scenarios. Because this research focuses 

on an analysis year (2030) that occurs prior to signifi cant divergence in 

emissions levels across the different scenarios, sensitivity analysis of results 

across the different emissions scenarios was not considered a high priority. 

In contrast, the signifi cant variability across GCM results using a single 

emissions scenario suggested a need to explore climate projections across 

a number of models to capture a range of possible climate impacts arising 

from a single projected path of emissions. 
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“Downscaling” refers to the process of translating the large-scale climate 

information that emerges from GCMs into fi ner temporal and spatial resolution. There are several methods available for downscaling GCM output; the 

downscaled data used in this study were generated by Jones, Thornton, and 

Heinke (2009). Each of the four datasets represents output from a different 

GCM running the SRES A1B emissions scenario (table 4). For more information on the climate projections and how the downscaled data were aggregated to regions suitable for the REAP analysis, see Appendix B.

Figure 2 shows the variation in mean annual maximum temperature and 

precipitation change between the base period and 2030 for each of the four 

climate projections across REAP regions. The Model for Interdisciplinary 

Research on Climate (MIROC) represented the most extreme change from 

the base period, in that it demonstrated the largest temperature increase and 

the most negative precipitation change. The Commonwealth Scientifi c and 

Industrial Research Organisation (CSIRO) and Max Planck Institute’s ECH 

scenarios represented the mildest change projections; both predicted a similar 

range of temperature increases across REAP regions, but the ECH scenario 

predicted slightly wetter conditions relative to the CSIRO projections. The 

Centre National de Recherches (CNR) projection was highly variable in both 

temperature increase and precipitation change across the REAP regions and 

represented a moderate national projection in terms of the severity of climate 

change predicted among the scenarios considered.

While the magnitudes of shifts in maximum temperatures differed across 

GCMs, regional patterns of temperature impact were somewhat similar (fi g. 

3). The models generally projected the most moderate temperature increases 

in the West and Southeast and more signifi cant temperature impacts in the 

Midwest and Northeast. That pattern of impact was roughly consistent with a 

composite of multiple IPCC model simulations generated for North America, 

which projected temperature increases from approximately 1 degree Celsius 

(oC) in the Southeast to more than 2 oC in northern Canada, with intermediate values over the rest of the contiguous United States (USCCSP, 2008).

In contrast to the consistent pattern of relative temperature impacts, there 

was little consistency in precipitation-change projections across GCMs 

Table 4

General circulation models adopted for use in this study

Model name Label Institution Reference

CNRM-CM3 CNR

Centre National de Recherches Meteorologiques 

(CNRM), Meteo France, France Déqué et al. (1994)

CSIRO-Mark 3.0 CSIRO

Commonwealth Scientifi c and Industrial Research 

Organisation (CSIRO) Atmospheric Research, Australia Gordon et al. (2002)

ECHam5 ECH Max Planck Institute for Meteorology, Germany Roeckner et al. (2003)

MIROC 3.2 MIROC

Center for Climate System Research (University of 

Tokyo), National Institute for Environmental Studies, 

and Frontier Research Center for Global Change 

(JAMSTEC), Japan K-1 Developers (2004)

CM3 = Climate Model, version 3.

ECHam5 = European Centre – Hamburg.

MIROC 3.2 = Model for Interdisciplinary Research on Climate.
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(fi g. 4). Some regions exhibited an increase in precipitation according to 

some models and a decrease in precipitation according to others. A few 

regions demonstrated a consistent direction of impact across models; the 

Pacifi c Northwest exhibited an increase in precipitation across all models, 

while the Texas/Louisiana region exhibited a decline in precipitation 

across all models. Nevertheless, even regions with a consistent direction 

of impact exhibited a wide range of estimated magnitudes across GCMs. 

Because there is no basis with which to assign probabilities to climate 

outcomes predicted across GCMs, crop yield impacts, adaptation potential, and aggregate system impacts were calculated and presented independently for each of the illustrative climate projections.

Quantifying Climate Change Impacts 

on Crop Yields 

Climate change is expected to impact crop growth and development through 

a number of pathways (see box, “Climate Change Impacts on Crop Yields,” 

p. 4). In this analysis, climate change impacts on crop yields were estimated 

using EPIC—a fi eld-scale biophysical model that uses a daily time step to 

simulate crop growth, soil impacts, hydrology, nutrient cycling, and pesticide fate under various cropping systems (e.g., tillage, crop rotation, soil and 

nutrient management) and weather scenarios. A random weather generator 

built into EPIC uses the average monthly climate information derived from 

the GCMs—minimum daily temperature (TMIN), maximum daily temperature (TMAX), and precipitation (PRCP)—to generate daily temperature and 

precipitation patterns for simulated crop growth in each REAP region. 

Figure 2

Estimated change in mean annual maximum temperature 

and precipitation for each REAP production region under 

the four climate change scenarios

Temperature change, 2000-2030 (degrees Celsius)

REAP = Regional Environment and Agriculture Programming model.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations and Jones, Thornton, and Heinke, 2010.

Precipitation change, 2000-2030 (mm/year)

CNR

CSIRO

ECH

MIROC

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-250 -200 -150 -100 -50 0 50 100 150 200
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To represent a range of possible weather scenarios associated with each GCM’s 

set of average monthly estimates, simulation results were run 10 times for 20 

years, using a different random weather seed for each run. Results from the 

fi rst 10 years of each run (a total of 100 years) were discarded to minimize the 

impact of initial soil conditions on yield and environmental impact estimates. 

Results from the remaining 100 years were used to calculate the average yield 

and environmental impact results associated with each production enterprise. 

Because variability estimates for future weather cannot be derived from either 

the original or the downscaled GCM climate output, weather variability—and 

therefore the incidence of extreme weather events—was held constant in this 

analysis across the baseline and future weather scenarios. 

For each production enterprise, EPIC was used to calculate a set of yield 

and environmental impact measures associated with region-specifi c weather 

assumptions and four sets of regional soils differentiated by highly erodible, non-highly erodible, with tile drainage, and without tile drainage. To 

calculate the impact of the climate change scenarios on crop growth in each 

region, the crop growth parameters and geophysical process parameters used 
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in EPIC’s simulations were held constant across the estimates generated using 

the baseline climate conditions and the projected climate conditions emerging 

from the GCMs. In moving from the baseline to the climate change projection scenarios, however, we assumed that ground-level CO2 concentrations 

increased from 381 parts per million (ppm) in the baseline to 450 ppm across 

GCM projections. EPIC calculated the impact of the increased atmospheric 

CO2 effect using a nonlinear plant response equation with crop-specifi c 

parameters (see Appendix C for a more detailed discussion of the impact of 

increased atmospheric CO2 levels in this analysis). The only other variables 

that differed between the baseline and the climate change yield estimates 

were the TMIN, TMAX, and PRCP variables.

Quantifying the Impacts of Climate Change 

and Adaptation Behavior Using REAP

Climate-induced changes in agricultural production were assessed by substituting into REAP the yield and cost estimates for production enterprises that 

were estimated in EPIC using new, regionally variable climate conditions 
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associated with climate projections. Yield estimates were again adjusted (by 

the same crop-specifi c adjustment factor) to account for exogenous increases 

in productivity up to 2030. Because climate change affects crops and regions 

differently, the relative productivity and economic value of regional production enterprises will change under the projected climate scenarios. Production 

enterprises that are economical under one climate regime may not be 

economical under another. 

Our analysis showed that in each climate change scenario, several historically 

established crop rotations were no longer economical to employ, possibly 

because the enterprise was marginally economical in the baseline and was 

sensitive to climate-induced changes in yield or price. Another economic 

driver may be that changes in other crop yields and/or prices make them 

more favorable to produce and force out crop rotations that do not experience improved yields or increased prices. Optimizing agricultural production 

levels and patterns under projected climate conditions produced a new pattern 

of production enterprises that refl ected changes in regional production levels, 

including shifts among crops, crop rotations, tillage used, and expansion or 

contraction of cropland.

Quantifying the Additional Impacts 

of Pest Distribution Change

Pests and diseases reduce crop yields through several means. Weeds often 

reduce yields by competing for external resources. Generally, there are 

few genetic sources of host plant resistance to weeds, with the exception of 

parasitic weeds that invade the roots of crop plants. Host plant resistance to 

insects is more common in fi eld crops, and host plant resistance is even more 

important in fi eld crops for many plant diseases, particularly fungal diseases. 

Whether measured in pounds of active ingredient or in pesticide costs, 

herbicides are the most widely used chemicals in U.S. fi eld crop production. 

Insecticide use has been common in corn and cotton. Fungicide use is quite 

low in fi eld crops, although it is much higher in fruits and vegetables (Osteen 

and Livingston, 2006; Padgitt et al., 2000).

Global climate change, at least in terms of average annual temperature, will 

have the effect of making production conditions in northern U.S. regions 

more similar to production conditions in southern regions. In southern 

regions, problems with pests, especially weeds—the most important type 

of crop pest in terms of pesticide expenditures and yield losses—are much 

more severe (Bridges, 1992). In this analysis, the assumption of a temperature 

change-induced migration of pest costs and impacts was used to estimate 

the potential additional pest-related impacts on yield losses and production 

patterns associated with changing climate conditions. Our analysis assumed 

that temperature is an important factor driving changes in the geographical 

distributions of pests and invasive species. This was a reasonable assumption 

for many biological organisms because average temperatures during winter 

months are important determinants of overwintering survival rates and 

because average annual temperatures are correlated with average temperatures during winter months (Hatfi eld et al., 2008). Changes in precipitation 

patterns also guide changes in the distribution of pests and invasive species. 

Estimating the direct impact of precipitation changes with consistency, 
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however, was beyond current modeling capacity. We instead focused on 

estimating the direct effect of average annual temperature changes, while 

accounting for annual weather variation over time and space.

We began by estimating quadratic relationships between average annual 

temperature and latitude for the REAP crop production regions for 2000 

and 2030 based on CNR, CSIRO, ECH, and MIROC temperature projections. These estimates were then used to estimate the percentage shift in each 

region’s latitude centroid—the latitude at the center of the region—associated 

with the respective temperature change projections. Those percentage shifts 

were used to characterize the extent of each region’s southward movement in 

latitude-temperature space (see Appendix D for more information). 

We then used Agricultural Resource Management Survey (ARMS)1 data 

for barley (2003), corn (1996, 2001, 2005), cotton (1997, 2003, 2007), oat 

(2005), rice (2000, 2006), sorghum (2003), soybean (1997, 2002, 2006), and 

wheat (2004) producers to estimate linear relationships between real pesticide expenditures per acre and latitude for each crop, while accounting for 

weather variation over time and 10 USDA National Agricultural Statistics 

Service (NASS) crop production regions.2 The estimates from this model 

were combined with the estimates used to characterize the southward movement of the REAP regions in latitude-temperature space (Appendix D, table 

5) to obtain percentage changes in pesticide expenditures and yield losses for 

each crop and for each region under climate change in 2030 relative to 2000. 

The percentage changes in pesticide expenditures and yield losses were then 

input into the REAP model to examine the pest-impact scenarios associated 

with climate change.

Generally, both yield loss and pesticide use increase with pest pressure 

(Livingston, Carlson, and Fackler, 2004; Hatfi eld et al., 2008). Because 

observations on yield loss due to pests are not available in the ARMS data, 

percentage yield-loss impacts were specifi ed as a constant multiple of the 

percentage pesticide-expenditure impacts. The constant multiple was the elasticity of yield loss with respect to pesticide applications (0.97), which was based 

on estimates of a yield-loss function for cotton reported by Livingston et al. 

(2007). While the elasticity of yield loss with respect to pesticide applications 

likely varies by crop—as well as over time, as new pesticides and genetically 

engineered (GE) crop varieties become available—we used the estimate for 

cotton to specify yield-loss impacts for all crops because cotton was the only 

U.S. crop for which reliable yield-loss estimates due to pests were reported. 

This method implicitly assumes that the shares of pesticide expenditures and 

yield losses associated with domestic and invasive pests, and the rate of introduction of new invasive species, would not change during 2000-30 relative to 

1996-2007. These assumptions were necessary because information was not 

available to determine the allocation of pesticide expenditures across both 

pest categories in the ARMS data and because impacts of climate change on 

invasive species introductions are diffi cult to predict. 

Estimates suggest that with changes in the distribution of pest populations for each climate-change scenario, pesticide expenditures and yield 

losses would increase for barley, corn, oats, sorghum, soybeans, and wheat; 

decline for cotton; and remain fairly constant for rice (table 5). The estimates 

1ARMS is a joint project of USDA’s 

Economic Research Service and 

the National Agricultural Statistics 

Service. Visit http://www.ers.usda.gov/

Briefi ng/ARMS/ for more information 

and to download summary statistics 

based on these data.

2Observations on average annual 

temperature and precipitation were not 

available in the ARMS data; therefore, we used a time index and NASS 

production-region fi xed effects to account for annual weather variation over 

time and space. We used the results 

reported in appendix table 6 to specify 

the pesticide-expenditure and yieldloss impacts used in the analysis.
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suggest that the largest impacts would occur in oats, which could experience 

increases in pesticide expenditures between 6 and 13 percent (for CSIRO and 

MIROC, respectively). The least deleterious impacts would occur in corn, 

which might experience increases in pesticide expenditures between 2 and 5 

percent (for CSIRO and MIROC, respectively).

Quantifying the Impacts of Drought-Tolerant Varieties

To complement the analysis of adaptive behavior of agricultural producers, 

we analyzed a case that represents the potential for technical change to 

provide additional adaptation opportunities and the implications of those 

opportunities on the magnitude or pattern of climate change impacts. There 

are many promising avenues of research on plant genetics, soil management, 

and inputs to production that may lead to advances that mitigate climate 

change impacts. We did not attempt to describe all the possible benefi ts of 

such research. We considered an illustrative case that introduces varieties for 

selected crops that can maintain yields under conditions of reduced precipitation, so-called “drought-tolerant” varieties, thereby reducing yield losses due 

to climate change for some crops in regions with low precipitation.

Table 5

Average percentage change in pesticide expenditures and yield losses across the REAP (Regional 

Environment and Agriculture Programming model) regions for each climate change scenario, by crop

Crop

ECH CSIRO CNR MIROC

Cost Yield loss Cost Yield loss Cost Yield loss Cost Yield loss

Percent

Barley 4.9 4.7 3.9 3.8 6.0 5.8 8.5 8.2

Corn 2.9 2.8 2.3 2.3 3.5 3.4 5.0 4.8

Cotton -1.0 -1.0 -0.8 -0.8 -1.2 -1.2 -1.7 -1.7

Oats 7.4 7.1 5.9 5.7 9.0 8.7 12.9 12.4

Rice -0.5 -0.4 -0.4 -0.4 -0.6 -0.5 -0.8 -0.8

Sorghum 3.9 3.8 3.2 3.1 4.8 4.6 6.8 6.6

Soybeans 4.2 4.1 3.4 3.3 5.1 4.9 7.3 7.0

Wheat 3.9 3.7 3.1 3.0 4.7 4.5 6.7 6.4

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Notes: Estimates based on the maximum likelihood estimates relating pesticide expenditures to latitude and the southward movements of the 

latitude centroids of each REAP region (see Appendix D). Percentage changes in yield losses are a constant multiple (0.9661) of the percentage changes in pesticide expenditures. The constant multiple is the elasticity of yield loss with respect to pesticide applications and is based on 

estimates and data reported by Livingston et al. (2007).
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Quantifying the Agricultural Impact of 

Climate Change and the Potential for 

Agricultural Adaptation

Applying information about the yield impact of climate change across 

projected production patterns in the United States is a straightforward, if 

naïve, approach to estimating the potential economic impact of climate 

change. This method assumes an unrealistic future in which farmers’ yields 

and returns are affected by climate change, but farmers fail to adapt their 

production decisions to changing climate conditions. Nevertheless, illustrating 

just such a scenario allows us to visualize regional and crop differences in the 

biophysical impacts of climate change and, as described later, to differentiate 

changes that take place in the agricultural sector due to biophysical impacts 

from those resulting from dynamic behavioral adaptation. 

We fi rst illustrate a “no adaptation” case and then provide the results of a 

more comprehensive assessment of climate change impacts, incorporating the 

impacts of farmer adaptation decisions in determining production and price 

patterns under changed climate conditions. A comparison between the “no 

adaptation” and “adaptation” cases illustrates the benefi ts of adaptation.

Climate Impact Analysis: No Adaptation

“No adaptation” case results were derived by putting new yield numbers 

into REAP but prohibiting the model from adjusting projected 2030 baseline acreage, tillage, or rotation allocations to crop production activities in 

response to the new climate-adjusted yields. REAP then used the baseline 

(i.e., no climate change) production patterns, together with the adjusted yield 

and environmental impact information, to calculate crop production, farmer 

income, price impacts, and environmental impacts under each future climate 

scenario. Note that, for this case, REAP eliminated farmer adaptation but 

retained the fl exibility to adjust the livestock sector, most notably with respect 

to its demand for feed grains, in response to changed production and price 

patterns. Changes in national productivity by crop due to climate change, 

assuming no adaptive behavior on the part of farmers, are shown in fi gure 5.

These crop yield averages refl ect the average impact of climate change on 

individual production enterprises (i.e., region/rotation/tillage combinations) 

weighted by the amount of crop acreage in that production enterprise, which 

remained constant across the climate projections in this case. Several interesting climate effects on crop productivity are evident here. Climate change 

impacts were most negative for corn and soybean productivity, though the 

least extreme scenario (ECH) produced an increase in crop yields for both. 

Several other crops experienced crop productivity increases for some or all 

of the scenarios, though the yield increases associated with the more extreme 

climate change scenarios (MIROC and CNR) were generally lower than 

those associated with the milder scenarios (ECH and CSIRO). While the 

impact of any temperature increase was generally negative, positive crop 

productivity impacts can arise both from benefi cial precipitation changes 

(increases in water-constrained regions) or from the CO2 fertilization effect 

projected when atmospheric carbon dioxide concentrations increase from 381 

to 450 parts per million (ppm). For several crops, the latter positive effects 
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outweighed the negative temperature-related losses for some or all of the 

projected climate projections.3

Aggregating crop productivity impacts at the national level, however, masked 

considerable variability in both crop productivity by region under the baseline 

and in regional impacts on productivity under the climate change scenarios. 

Disaggregating the results for corn to the level of the farm production region 

produced the results shown in fi gure 6. While the productivity results for the 

Corn Belt drove the pattern of national averages shown in fi gure 5 (because 

the Corn Belt accounts for 53-56 percent of U.S. corn production under 

these scenarios), there were regional differences in corn’s response under a 

given climate scenario. In some regions, one or both of the milder climate 

change scenarios actually increased corn yields. Furthermore, corn production increased under even the extreme climate scenarios in the minor cornproducing regions of the Pacifi c and Mountain States and the Southern Plains 

region. A portion of corn production in those regions is irrigated and, therefore, less sensitive to precipitation losses from climate change but also mildly 

responsive to carbon dioxide fertilization gains. 

In a situation with fewer interacting parts, price results might mirror productivity results; when national average crop productivity decreases (increases), 

the price of that crop increases (decreases) (fi g. 7). Since crop and livestock markets are integrated, however, this simple dynamic did not play 

out for several crops and climate scenarios. Because corn prices go up in 

every scenario, there was always an incentive to substitute away from corn 

in livestock diets, which has implications for the price of other grains and 

feed meals. The signifi cant corn price increases in the CNR and MIROC 

3For further information on the 

sensitivity of EPIC’s results to different elements of climate change, see 

Appendix C.

Figure 5

Changes in national average crop yield without adaptation, 

by climate change scenario

Percent change in yield from baseline

Cwt=Hundredweight.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

CNR

CSIRO

ECH

MIROC

Barley (bushels)

Corn (bushels)

Cotton (480-lb bales)

Hay (tons)

Oats (bushels)

Rice (cwt)

Silage (tons)

Sorghum (bushels)

Soybeans (bushels)

Wheat (bushels)

-30

-20

-10

0

10

20

30

40

50
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scenarios, in particular, appeared to pull up soybean, oat, sorghum, and 

barley prices, despite fi eld productivity increases for some of those crops. 

Productivity, and therefore supply, of oats increased under every scenario, 

but prices also increased under all but the ECH scenario. Similarly, barley’s 

substitutability with the other feed grains in livestock diets led to increased 

Figure 6

Regional differences in national average corn yield without adaptation, 

by farm production region and climate change scenario

Percent change from baseline

AP = Appalachia, CB = Corn Belt, DL = Delta, LA = Lake States, MN = Mountain, 

NP = Northern Plains, NT = Northeast, PA = Pacific, SE = Southeast, SP = Southern Plains.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

CNR

CSIRO

ECH

MIROC

AP CB DL LA MN NP NT PA SE SP US

-20

-15

-10

-5

0

5

10

15

20

25

Figure 7

Price changes relative to the “no climate change baseline,” 

by climate change scenario

Percent change in crop price from baseline

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

CNR

CSIRO

ECH

MIROC

-20

-15

-10

-5

0

5

10

15

20

25

Wheat

Barley

Corn

Cotton Oats

Hay Rice

Silage

Sorghum

Soybeans
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barley demand and a demand-induced price increase under the MIROC 

scenario that persisted despite increasing supply.

The productivity impacts illustrated in this section refl ect only EPIC’s yieldchange calculations based on changing climate conditions, while the price 

impacts refl ect a limited set of interacting demand-and-supply forces across 

agricultural sectors. Projecting potential climate change impacts on the crop 

sector, however, requires a more comprehensive analysis to capture how 

farmers may respond to biophysical impacts in their production decisions. In 

the following sections, we discuss how farmers might adapt to the biophysical 

impacts of climate change and the implications of such production adjustments for aggregate crop production, prices, agricultural acreage, and a suite 

of environmental indicators under changing climate regimes. 

Climate Change Impacts When Farmers Adapt Crop 

Rotations, Tillage, and Land Use 

The economic value of planting specifi c crops in each region changed in 

response to the new production conditions, since yields and costs did not 

change uniformly in magnitude or direction for all regions in our scenarios. 

In some regions, adaptation to climate change resulted in reduced planted 

acreage, while planted acreage in other regions increased. Regional production effects refl ected both changes in yield and planted acreage. Differences 

in production levels, coupled with demand response to substitute crops, in 

turn drove changes in crop prices. The combination of changes in acreage, 

yield, and price infl uenced the degree to which farm revenues responded in a 

region. In this section, we report results from the REAP model that describe 

the economic and environmental impacts of the climate change scenarios on 

U.S. crop production, taking into account how farmers may adjust their crop 

and tillage decisions. The results are shown relative to the baseline projection 

(assuming no climate change) for U.S. agriculture in 2030.

Regional Shifts in Planted Acreage for Selected Crops

Each of the four climate change scenarios demonstrated a small increase in 

total planted acreage compared with baseline acreage levels, though there 

is variability in the direction of acreage change by region (fi g. 8). The total 

acreage change, though relatively small compared with the baseline acreage, 

was composed of changes in the acreage planted to individual crops. The 

individual crops showed a much wider range across climate change scenarios, 

following differences in productivity and regional redistribution (table 6). 

Corn acres increased in all scenarios, refl ecting the decline in corn yields 

(see fi g. 5) and the need for additional acreage to compensate. Response 

of other crops varied by scenario, with the ECH and CSIRO scenarios 

showing a reduction in wheat acres that corresponds to the larger wheatyield increases in these scenarios. Soybean acres declined in the CNR and 

MIROC scenarios; despite higher soybean prices, acreage decline likely 

refl ects a decrease in the relative returns to soybean production arising from 

the signifi cant yield decline under the warmer climate scenarios. The corresponding corn price increase in the MIROC scenario keeps soybean acres 

from declining further as soybeans are often produced in rotation with corn, 

particularly in the Corn Belt.
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Table 6

Total U.S. acreage change, by climate change scenario

Crop ECH CSIRO CNR MIROC

Percent change

Total 0.6 0.6 0.2 1.0

Corn 1.7 2.8 3.0 4.2

Wheat -1.1 -0.2 1.0 0.8

Soybeans 1.4 1.0 -2.8 -1.8

Other crops -0.1 -1.5 -0.2 0.5

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Figure 8

Changes in total regional planted acres from “no climate change baseline”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

5.9% to 8.5% 3.3% to 5.9% 0.7% to 3.3% -2.0% to 0.7% -4.6% to -2.0% -7.2% to -4.6% -9.8% to -7.2%
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Even though total U.S. acreage planted increased in all scenarios, no individual region showed an increase in planted acreage across all scenarios. 

While the variation in total acreage change was small across scenarios from 

a national perspective, the regions showed different degrees of response. 

Acreage in the Corn Belt was the least sensitive to climate change, with total 

changes ranging from -2.7 to 1.4 percent. In contrast, the Delta region showed 

a range of change between -9.8 and 5.0 percent. The sensitivity to change 

refl ects the capacity of the region to economically shift to a different crop 

mix, indicating crop and production practice substitution possibilities and the 

larger regional yield changes relative to the national changes for each crop. 

Figures 9-11 illustrate the change in planted acreage by climate change 

scenario for the three major fi eld crops (corn, wheat, and soybeans). As with 

the national total, corn acreage in the Corn Belt was the least sensitive (-1.3 to 

1.1 percent). For each of the other regions, at least one scenario demonstrated 

at least a 10-percent change in acreage from the baseline. The Southern 

Plains increased corn acreage in all scenarios, with the Delta and Pacifi c 

regions reducing corn acres in all scenarios. 

Figure 9

Changes in regional planted corn acres from “no climate change baseline”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

33.0% to 46.2% 19.8% to 33.0% 6.7% to 19.8% -6.5% to 6.7% -19.7% to -6.5% -32.9% to -19.7% -46.1% to -32.9%

ECH

CSIRO MIROC

CNR
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The Corn Belt also showed the smallest change in wheat acres (0.4 to 

2.4 percent). The Lake States and Corn Belt increased wheat acres in all 

scenarios; all other regions showed both increases and decreases across 

scenarios. Climate change induced soybean acres to move into the Northern 

and Southern Plains (see fi g. 11). Once again, the Corn Belt showed the 

smallest range in soybean acreage change (-5.6 to 0.8 percent); by contrast, 

change in soybean acres in the Delta region ranged from -19.2 to 11.4 percent.

Corn acreage increased in the combined Northern regions (Corn Belt, Lake 

States, Northern Plains, and Northeast) in all scenarios, while corn acres in 

the combined Southern regions (Appalachian, Delta, Southeast, and Southern 

Plains) declined in the ECH and CSIRO scenarios and increased in the CNR 

and MIROC scenarios. The same relationship holds for wheat acres. Taken as a 

group, the Southern regions were more sensitive to weather-induced yield change 

than were the Northern regions. This trend was indicative of the larger range in 

EPIC-derived yields in the Southern regions under the climate change scenarios.

Figure 10

Changes in regional planted wheat acres from “no climate change baseline”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

34.7% to 45.6% 23.8% to 34.7% 12.9% to 23.8% 2.1% to 12.9% -8.8% to 2.1% -19.7% to -8.8% -30.6% to -19.7%

ECH

CSIRO MIROC

CNR
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Changes in Crop Prices and Regional Farm Revenue

Crop price changes were highly scenario-specifi c, although there were some 

features common to the results. The CNR and MIROC scenarios led to higher 

corn and soybean prices than under the baseline, whereas the CSIRO and ECH 

scenarios generally resulted in lower prices (except for a small price increase in 

soybeans in CSIRO) (table 7). Corn and soybean yields were lower in the CNR 

and MIROC scenarios in all regions, while the CSIRO and ECH scenarios 

showed increased yields in some regions. Wheat prices declined in all scenarios. 

Wheat and corn prices were generally less sensitive to changes in climate than 

soybean prices, mainly as a result of smaller projected sensitivity of wheat yields 

to temperature and rainfall and the poor substitutability between wheat and corn 

in the diets of livestock within REAP. While REAP’s existing diets refl ect feed 

combinations that historically have been used in the livestock sector, signifi cant 

feed price changes may induce changes within the livestock sector in popular diet 

combinations. Our analysis did not capture that dynamic.

Climate change reduced returns to corn in all scenarios relative to the 

baseline, although less so in the higher temperature/precipitation change 

scenario, where price increases helped support producer revenue (table 8). 

Because of the positive effect of climate change on cotton yields, cotton 

Figure 11

Changes in regional planted soybean acres from “no climate change baseline”

Note: Soybeans not cultivated in Pacific and Mountain regions.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

24.0% to 31.2% 16.8% to 24.0% 9.6% to 16.8% 2.4% to 9.6% -4.8% to 2.4% -12.0% to -4.8% -19.2% to -12.0%

ECH

CSIRO MIROC

CNR
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returns increased signifi cantly in all scenarios. Returns to other crops 

varied by scenario. Returns in the Corn Belt mimicked changes in precipitation and temperature, whereas returns in other regions did not necessarily 

mirror the same trend (table 9). The more extreme scenarios generally led 

to lower returns, but not for all regions. Nationally, the milder scenarios 

led to an increase in returns, whereas the more extreme scenarios led to 

a decline in returns. From the perspective of crop returns, the Southern 

Plains was the most robust, even though the regional change in acreage 

was large compared with other regions. This result refl ects the relatively 

higher returns to cotton production under climate change, and the ability of 

farmers in the Southern Plains region to reallocate production resources to 

minimize the regional impact on profi tability.

Table 7

Change in crop prices with adaptation relative to the “no climate 

change” baseline

Crop ECH CSIRO CNR MIROC

Percent change

Corn -2.2 -2.1 3.7 6.0

Wheat -1.6 -5.9 -0.8 -1.0

Soybeans -3.5 0.3 7.6 22.1

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Table 8

Change in annual returns to crop production from “no climate 

change baseline”

Crop ECH CSIRO CNR MIROC

$ Million 

Corn -742 -839 -33 -223

Wheat -10 332 -265 -456

Soybeans 1,361 -180 -2,772 -3412

Cotton 1,135 1,081 1,474 1,266

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Table 9

Change in annual returns to crop production from “no climate 

change baseline”

Region ECH CSIRO CNR MIROC

$ Million 

Corn Belt -1,114 -2,165 -2,112 -4,053

Delta 904 167 -521 -146

Lake States 41 902 1,001 -37

Northern Plains 1,256 1,671 -914 255

Southern Plains 418 322 7 681

U.S. total 3,619 2,165 -332 -1,465

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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Changes in Environmental Outcomes

Shifts in crops and production practices as a result of climate-induced 

changes in crop yields will have an infl uence on the environmental impacts 

associated with agricultural production. Changes in total acreage, in tandem 

with the redistribution of crop rotation/tillage practices, will affect regional 

soil and water quality through nutrient loss and soil erosion. Increases in 

environmental impacts caused by acreage expansion in one region may 

not be offset by acreage decreases in other regions, resulting in aggregate 

national increases in water quality impacts. Table 10 depicts the changes in 

nitrogen loss (leaching and runoff to ground and surface water) and rainfallrelated soil erosion (specifi cally sheet and rill erosion) compared with the 

baseline, along with the change in U.S. planted acres. Total nitrogen lost to 

water (measured as nitrogen deep percolation and runoff at the fi eld edge) 

increased in all scenarios, which follows from the general increase in acreage 

nationally. Increased nitrogen loss was not uniform across the country, with 

the MIROC scenario resulting in the most widespread changes (fi g. 12). The 

Corn Belt, Northern Plains, and Southeast regions all increased nitrogen lost 

to water in all scenarios.

Soil lost to water erosion also increased in all but the ECH scenario, and this 

measure was generally less sensitive to climate scenario than the measure 

for nitrogen loss to water (table 10). Changes in the Corn Belt and Northern 

Plains dominated the national erosion change, as a result of shifts in crop 

rotation and tillage and an increase in planted acres (fi g. 13). 

How Farmer Adaptation Affects Measured Impacts 

of Climate Change

In the no-adaptation case, farmers did not adjust production decisions in 

response to changes in expected yields across crops. As a result, regions may 

overplant crops whose returns have declined relative to other potential crops, 

while underplanting crops that have become relatively more profi table. In the 

adaptation case, in contrast, farmers adjusted their land use, crop rotations, 

and tillage regimes in response both to changes in climate and adjustments 

in market conditions resulting from climate effects. Below, we examine how 

incorporating farmer adaptive response to climate change affects measured 

economic and environmental impacts.

Table 10

Change in total U.S. planted acreage and select environmental measures with adaptation relative to the “no climate change” baseline 

Environmental measure ECH CSIRO CNR MIROC

Percent

Total acreage 0.6 0.6 0.2 1.0

Nitrogen loss to water 1.4 1.5 2.1 5.0

Sheet and rill erosion -0.9 0.6 0.9 1.2

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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Regional Shifts in Planted Acreage for Selected Crops

Adaptation includes the various strategies farmers use to adjust their production decisions (e.g., shifts in crops or crop rotations) in response to absolute 

and relative changes in yield and management costs. Figure 14 illustrates 

the redistribution of planted acreage that resulted from adaptation for this 

analysis. In most region/scenario combinations, adaptation led to an increase 

in corn acreage (fi g. 15). Failing to adapt (no adaptation case) restricted 

the supply of corn, which led to higher consumer prices. When farmers are 

permitted to adapt, they respond to high corn prices by changing production 

in favor of corn. Thus, farmer behavior can moderate climate change impacts 

on production by diverting productive resources to crops whose loss most 

negatively impacts other farm-sector stakeholders. 

Changes in Crop Prices and Regional Farm Revenue

Although national welfare (the sum of economic benefi ts to consumers plus 

benefi ts to producers) increases when farmers have the fl exibility to adapt, 

the benefi ts of adaptation differ across regions and between consumers and 

Figure 12

Regional change in total nitrogen loss to water from “no climate change baseline”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

-10.3% to -5.5% -5.5% to -0.7% -0.7% to 4.1% 4.1% to 8.9% 8.9% to 13.7% 13.7% to 18.5% 18.5% to 23.3%

ECH

CSIRO MIROC

CNR
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Figure 13

Regional change in sheet and rill erosion from “no climate change baseline”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

-44.2% to -35.7% -35.7% to -27.2% -27.2% to -18.7% -18.7% to -10.2% -10.2% to -1.7% -1.7% to 6.8% 6.8% to 15.3%

ECH

CSIRO MIROC

CNR

Table 11

Price difference from “no adaptation case” to “adaptation case,” 

by crop

ECH CSIRO CNR MIROC

Percent change 

Corn -3.2 -3.2 -3.6 -3.9

Sorghum -1.1 -1.8 -1.1 -1.2

Barley 2.1 2.6 -1.6 -2.9

Oats -0.6 -0.4 -8.7 -7.1

Wheat 0.2 -3.0 -0.7 -0.6

Rice 0.0 -0.3 -0.4 -0.3

Soybeans -0.6 -0.1 0.7 1.9

Cotton -8.2 -5.5 -5.7 -9.0

Silage 0.9 0.7 0.5 0.3

Hay 0.3 0.6 0.5 0.8

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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producers. Consumers generally benefi ted from the process of adaptation. 

Prices (a measure of consumer benefi t) were generally lower if all farmers 

adapted (table 11), which moderated consumer expenditure impacts of 

climate change and increased consumer welfare measures relative to the case 

where adaptation was not permitted. 

Producers were not necessarily better off as a result of adaptation, however. 

For some regions, nationwide adaptation led to lower returns relative to not 

adapting by, for instance, driving down the price of a major crop in that 

region. Returns to crop production in the Corn Belt did not benefi t from 

nationwide adaptation (table 12); this is the consequence of smaller acreage 

changes in the Corn Belt relative to other regions combined with the lower 

prices that resulted from adaptation. 

Changes in Environmental Outcomes

Table 13 illustrates the consequences of adaptation with respect to selected 

environmental measures when compared with the fi xed-acreage, no adaptation case. The percent increase in nitrogen loss was greater than the change 

Figure 14

Regional change in total acreage, from “no adaptation case” to “adaptation case”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Note: Figure 14 is identical to figure 8 because production acreage is fixed in the “no adaptation case” at the level of production acreage 

in the “no climate change baseline”. 

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

5.9% to 8.5% 3.3% to 5.9% 0.7% to 3.3% -2.0% to 0.7% -4.6% to -2.0% -7.2% to -4.6% -9.8% to -7.2%
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Figure 15

Regional change in corn acreage from “no adaptation case” to “adaptation case”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Note: Figure 15 is identical to Figure 9 because corn acreage is fixed in the “no adaptation case” at the level of corn acreage in the

“no climate change baseline”. 

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

33.0% to 46.2% 19.8% to 33.0% 6.7% to 19.8% -6.5% to 6.7% -19.7% to -6.5% -32.9% to -19.7% -46.1% to -32.9%

Table 12

Direction of change in returns to crop production from “no adaption 

case” to “adaptation case,” by region

ECH CSIRO CNR MIROC

Appalachian 

Corn Belt 

Delta 

Lake States 

Mountain 

Northern Plains 

Northeast 

Pacifi c 

Southeast 

Southern Plains 

U.S. total 

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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in acreage in all scenarios, suggesting that climate change adaptation induced 

crop production to shift into areas and production practices with greater 

water-quality impacts. Climate change-induced crop reallocation, however, 

signifi cantly reduced nitrogen loss in the Pacifi c and Mountain regions while 

resulting in substantial increases in the Southern Plains and mixed impacts in 

other regions (fi g. 16). Even the more extreme scenarios showed regions where 

nitrogen loss declined, despite a 1.8- to 3.6-percent increase in losses nationally (table 13). Regional impacts on soil loss to water erosion followed a similar 

pattern to that of nitrogen loss (fi g. 17), although the national change in the 

erosion measure was similar to, not greater than, the national acreage change.

Impact of Changes in Pest Prevalence 

on Crop Production Under Climate Change

Our initial REAP analyses of the climate change scenarios considered only the 

crop-yield differences resulting from changes in prevailing precipitation, temperature, and carbon dioxide concentration. Additional cost and yield implications of 

changes in pest prevalence (weed, insect, and plant disease) are likely to occur 

with climate change. Costs and yields were modifi ed for each climate change 

Figure 16

Regional change in nitrogen loss to water from “no adaptation case” to “adaptation case”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

-13.1% to -9.2% -9.2% to -5.2% -5.2% to -1.3% -1.3% to 2.7% 2.7% to 6.6% 6.6% to 10.6% 10.6% to 14.5%
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scenario, and the results were compared with the climate change scenario results 

for the case where additional pest impacts were not considered, allowing us to 

examine the additional contribution of climate change-induced pest pressure to 

potential changes in agricultural production and commodity markets.

Table 13

Change in total U.S. planted acreage and select environmental 

measures from “no adaptation case” to “adaptation case”

Environmental measure ECH CSIRO CNR MIROC

Million acres

Total acreage 1.8 1.8 0.8 3.2

Percent change

Total acreage 0.6 0.6 0.2 1.0

Nitrogen loss to water 4.3 4.0 1.8 3.6

Sheet and rill erosion 5.4 4.3 4.3 1.4

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Figure 17

Regional change in sheet and rill erosion from “no adaptation case” to “adaptation case”

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

-16.3% to -11.6% -11.6% to -6.9% -6.9% to -2.1% -2.1% to 2.6% 2.6% to 7.3% 7.3% to 12.0% 12.0% to 16.7%

ECH

CSIRO MIROC

CNR
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An increase in pest prevalence uniformly reduced yields across all crops 

(except cotton and, to a lesser extent, rice) in all regions. Thus, while the 

climate change scenarios described in the previous section showed some 

regions with yield increases for some crops, increased pest presence eliminated some, if not all, of those yield increases and reduced even further the 

projected yields that declined under climate change. The geographic and 

crop-specifi c nature of pest impacts led to differing implications for regional 

crop production.

Regional Shifts in Planted Acreage for Selected Crops

Table 14 shows the national outcome on total acreage for select crops when 

farmers account for yield and cost effects from increased pests. All scenarios 

showed an increase in total acreage planted to compensate for the yield reductions from increased pests. The two higher temperature-change scenarios—

CNR and MIROC—exhibited the largest acreage impact. For soybeans, there 

was a general decrease in acreage, except in the highest temperature-change 

scenario. Regional acreage changes varied for select crops. Pest changes 

reduced acreage for all crops in the Corn Belt, whereas other regions had a 

mixed response (fi g. 18). Corn acreage increased in all regions except the Corn 

Belt, where acreage remained fairly constant (fi g. 19). Wheat acreage changes 

varied regionally, with acreage declining generally in the North Central United 

States and increasing in the West and Southwest (fi g. 20). Wheat became 

more important in the Southeast in the CNR and MIROC scenarios. Soybeans 

shifted away from the Corn Belt and into the Northern and Southern Plains in 

all scenarios (fi g. 21).

Changes in Crop Prices and Regional Farm Revenue

Additional pest pressure resulted in price increases compared with the “adaptation case” without pest effects for all crops (except cotton) (table 15). Corn 

and soybean price changes were greater than the changes in wheat price in 

all scenarios. Regional and national returns to crop production were also 

affected (table 16). Additional increased pest pressure reduced returns from 

the adaptation-only level in all scenarios. The magnitude of the changes in 

returns associated with additional pests increased as the scenarios became 

more extreme, indicating that additional pests compound the temperatureand precipitation-induced yield changes as climate diverged from current 

conditions. In the Corn Belt, the impact of additional pests was insensitive 

Table 14

National change in planted acres from 2030 climate change 

scenarios as a result of changes in pest distribution

Crop ECH CSIRO CNR MIROC

Million acres

Total 2.1 0.9 4.2 5.6

Corn 1.0 0.6 2.2 2.0

Wheat 1.3 0.7 1.7 2.4

Soybeans -0.9 -1.2 -0.2 0.4

Cotton 0.1 0.0 0.0 0.1

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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Figure 18

Regional change in total planted acres from 2030 climate change scenarios as a result 

of changes in pest distribution 

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

4.8% to 6.0% 3.7% to 4.8% 2.6% to 3.7% 1.5% to 2.6% 0.4% to 1.5% -0.7% to 0.4% -1.8% to -0.7%

ECH

CSIRO MIROC

CNR

Table 15

Price difference from 2030 climate change scenarios as a result of 

changes in pest distribution

Crop ECH CSIRO CNR MIROC

Percent change

Corn 4.4 3.9 4.3 6.2

Sorghum 1.2 1.6 0.3 0.7

Barley 4.5 3.2 4.7 7.6

Oats 6.7 4.1 9.5 10.2

Wheat 1.5 1.7 1.7 2.6

Rice 0.0 0.0 0.0 0.0

Soybeans 3.1 2.6 5.1 9.1

Cotton -0.8 -0.6 -0.9 -1.4

Silage 1.0 0.8 1.5 2.3

Hay 2.2 1.7 2.6 3.7

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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Figure 19

Regional change in planted corn acres from 2030 climate change scenarios as a result 

of changes in pest distribution 

Note: The Pacific region’s acreage change was greater than 10 percent in ECH, CSIRO, and MIROC. 

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

7.0% to 8.5% 5.6% to 7.0% 4.2% to 5.6% 2.7% to 4.2% 1.3% to 2.7% -0.1% to 1.3% -1.6% to -0.1%

Table 16

Change in annual returns to crop production from 2030 climate 

change scenarios as a result of changes in pest distribution

Region ECH CSIRO CNR MIROC

$ Million

Corn Belt -462 -444 -568 -510

Delta -61 -38 -10 52

Lake States -322 -218 -502 -618

Northern Plains -416 -280 -760 -997

Southern Plains -114 -117 -181 -184

U.S. total -1,903 -1,471 -2,604 -3,007

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.
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to the climate conditions because of the relatively smaller temperature range 

across climate scenarios compared with other regions.

Changes in Environmental Outcomes 

Increased pest pressure on yields generally resulted in an increase in nitrogen 

losses to water (fi g. 22), a consequence of the increase in planted acreage required 

to make up for the lower yields. Soil lost to water erosion also increased in 

response to the increased acreage planted (fi g. 23). The impact of pests on soil 

erosion is generally less than on nitrogen loss, evident by the range of change 

over the scenarios. Nitrogen loss ranged from -15 to 15 percent over the climate 

change scenarios, whereas the range of soil erosion impacts was less (-3 to 10 

percent), with the Northern Plains and Mountain regions showing an increase in 

all scenarios. 

Figure 20

Regional change in planted wheat acres from 2030 climate change scenarios as a result of changes 

in pest distribution

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

15.1% to 18.6% 11.6% to 15.1% 8.1% to 11.6% 4.6% to 8.1% 1.1% to 4.6% -2.4% to 1.1% -5.9% to -2.4%
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Figure 21

Regional change in planted soybean acres from 2030 climate change scenarios as a result 

of change in pest distribution

Note: Soybeans not cultivated in the Pacific and Mountain regions.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

4.4% to 5.5% 3.3% to 4.4% 2.2% to 3.3% 1.1% to 2.2% -0.1% to 1.1% -1.2% to -0.1% -2.3% to -1.2%

ECH

CSIRO MIROC

CNR
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Figure 22

Regional change in nitrogen lost to water from 2030 climate change scenarios as a result 

of changes in pest distribution 

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

-14.7% to -10.4% -10.4% to -6.2% -6.2% to -2.0% -2.0% to 2.2% 2.2% to 6.5% 6.5% to 10.7% 10.7% to 14.9%

ECH

CSIRO MIROC

CNR
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Figure 23

Regional change in sheet and rill erosion from 2030 climate scenarios as a result of changes in pest distribution

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

-3.2% to -1.3% -1.3% to 0.6% 0.6% to 2.6% 2.6% to 4.5% 4.5% to 6.4% 6.4% to 8.3% 8.3% to 10.2%

ECH

CSIRO MIROC

CNR
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Lessening the Impact of Climate Change 

Through Technical Change: Targeting Crop 

Breeding for Adaptation to Climate Stress

We previously considered the regional effects of temperature and precipitation changes on crop yields, and then examined how potential shifts in the 

geographic distribution of pests and diseases might further impact crop yields 

and farmer adaptation decisions. We now consider how technical changes 

provide additional adaptation opportunities and the implications of those 

opportunities on land use, crop production, commodity markets, and environmental quality. We fi rst analyzed current crop genetic research addressing 

particular production constraints accompanying climate change, particularly 

heat, drought, and pests/diseases. We then designed and analyzed a case 

exploring the potential effects of new drought-tolerant crop varieties on the 

likely impacts of climate change. 

Adoption of Plant Varieties With Greater Tolerance 

to Climate Change-Related Stresses

Plant genetic resources might be used as an adaptation strategy in a number 

of ways: 

• One crop species may simply replace another in response to changing 

growing conditions within a region. Existing crop varieties may also be 

used in new locations as conditions change. 

• New varieties can be developed by plant breeders to improve tolerance 

to heat, drought, and pests/diseases, using classical breeding techniques, 

molecular methods (e.g., genetic engineering), or some combination of 

the two. These new plant varieties may also respond better to other adaptive management technologies, such as those related to irrigation and 

tillage (Rosenzweig and Hillel, 1995). 

Some changes in genetic resource use may come from individual farmers using 

existing resources and technologies. Development of new crop varieties and 

many other technological advances will require the involvement of the formal 

agricultural research system. Various institutions, both public and private 

(including gene banks and plant breeding research stations), are pioneering the 

use of existing varieties in new locations, developing new varieties, and testing 

the effi cacy of new varieties in conjunction with other adaptive strategies. 

Potential Adaptive Benefi ts From Genetic Resource Use 

Heat Tolerance

Tolerance to temperatures (extreme heat or cold) is a complex trait governed 

by multiple genes that involve a number of physiological traits and metabolic 

pathways. At present, few public and private sector research resources have 

been devoted to developing heat-tolerant crop cultivars. Hatfi eld et al. (2008) 

discussed the role of “failure temperatures” above which yields for different 

crops fall to zero. In a recent review, Wahid et al. (2007) concluded that 

“there are a few examples of plants with improved heat tolerance through the 
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use of traditional breeding protocols,” but that “the success of [the] genetic 

transformation approach has been thus far limited. The latter is due to limited 

knowledge and availability of genes with known effects on plant heat-stress 

tolerance, though these may not be insurmountable in the future.” 

Advances are being made in heat-tolerance research, however. A number 

of studies supported genetic variability for heat tolerance in important fi eld 

crops and suggested particular selection criteria that might enhance breeding 

for heat tolerance (e.g., Maestri et al., 2002; Martineau et al., 1979; Radin et 

al., 1994; ur Rahman et al., 2004; Reynolds et al., 1994). Other researchers 

have focused on targeted changes in plant metabolism, such as improved 

photosynthetic performance at higher temperatures (Parry et al., 2011). 

Nevertheless, the combination of long research lags and limited research 

investment suggests that the probability of substantial development of 

commercially viable heat-tolerant crops by 2030 is relatively low. 

Carbon Dioxide Responsiveness 

Evidence suggests that a portion of crop yield gains in the past may be attributed to yield response to increasing atmospheric CO2 (McGrath and Lobell, 

2011). Plant breeders may have inadvertently selected for response to CO2

fertilization, which raised the question of whether breeders might wish to 

select for greater response deliberately. Ziska et al. (Ziska and Blumenthal, 

2007; Ziska, 2008; Ziska and McClung, 2008) suggested that: 

• In some cases, newer varieties did not appear to be more responsive to atmospheric carbon dioxide fertilization than older varieties of the same crop; 

• Some genetic variability for this trait—response to atmospheric CO2—

did exist within cultivated species or wild relatives; and 

• The relative ease of incorporating such a trait may vary by crop. 

Pests and Diseases

The fi rst generation of genetically engineered (GE) crops for commercial use 

was bred specifi cally to control for pests (particularly insects) and disease as 

well as to improve herbicide tolerance. Herbicide-tolerant crops introduce a 

genetic element into weed control by making the crop tolerant of herbicides, 

such as glyphosate, which are then used to control weeds. As a result, using 

GE crops has reduced pesticide use (Fernandez-Cornejo and Caswell, 2006). 

Genetic engineering for disease resistance has also been explored (Mourges 

et al., 1998; Punja, 2001) but, to date, a disease-resistant GE crop has not 

been commercialized.

A great deal of money has been invested in both public and private research 

for disease/pest-tolerant or resistant crops. As a result, creating an adaptive 

genetic resource may reduce some of the negative impacts of the disease/

pest landscape likely to accompany climate change. For example, different 

forms of herbicide tolerance incorporated into crop varieties may be part of 

the management response if weeds become a more signifi cant problem (Duke 

and Powles, 2008). 
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New threats may also emerge with climate change. Weeds may compete 

increasingly with crops if their growth is more responsive to CO2 fertilization than the crop, or diseases may expand their ranges as alternate hosts 

also expand their range (e.g., soybean rust). Violent storms may have aided 

the spread of rust spores, fi rst from Asia to Africa, then to South America, 

and fi nally to the United States. The farm sector’s fi rst line of defense typically has been fungicides, with potentially large increases in fungicide use 

tempered by changes in cropping patterns (Livingston et al., 2004).4 USDA 

coordinated early soybean rust surveillance and warning networks that also 

reduced the impact of the disease (Roberts et al., 2006). 

Drought Tolerance

Drought tolerance, like heat tolerance, is a complex genetic trait. Over the past 

half century or more, “selection for high yield in stress-free conditions has, to 

a certain extent, indirectly improved yield in many water-limited conditions” 

(Cattivelli et al., 2008). U.S.-grown corn is one example of this phenomenon, 

where more than 50 years of selection in multi-environment trials has increased 

grain yield under drought conditions (Campos et al., 2004). Selection for yield 

stability may have played some role in improving performance under stress 

conditions. Attempts to improve drought tolerance directly through conventional plant breeding date back to the 1970s, if not earlier. More recently, 

molecular and genomic analyses identifi ed gene networks that may be important in drought stress (Shinozaki and Yamaguchi-Shinozaki, 2007). In applied 

breeding, seed companies have increased their efforts to develop droughttolerant crops, particularly corn, and several companies have placed new, 

conventionally bred drought-tolerant hybrids on the market. Large seed companies are also using transgenic5 means to develop drought-tolerant crops. 

The fi rst transgenic drought-tolerant corn hybrids received regulatory 

approval in 2011, which will be followed by extensive fi eld testing in late 

2012; these hybrids may be marketed as early as 2013, pending regulatory 

approvals.6 These hybrids may have been altered with genes from bacteria 

that regulate metabolic pathways for cold shock response; in plants, they may 

regulate pathways for other stresses, such as drought (Castiglioni et al., 2008). 

Potential yields from transgenic drought-tolerant corn were shown to increase 

by approximately 10 percent under managed stress environmental testing, and 

up to 15 percent under dryland conditions in environments where the average 

control yield was about 78 bushels/acre (Castiglioni et al., 2008).

Alternatively, Edmeades (2008) hypothesized that drought-tolerant corn 

had the potential to improve every 5 years over a 20-year period based on 

a combination of conventional selection, marker-aided selection, and a new 

transgenic event. Edmeades calculated yield improvement above a base 

yield of 48 bushels/acre to refl ect conditions in some developing countries. 

These research assumptions projected a tripling of yield over 20 years. Such 

estimates might be used as the rough basis for lower and upper bounds on 

achievable improvements in drought tolerance over a given period.

Current Research Emphases

Several of the physical factors that accompany climate change can infl uence 

crop yields, and both genetic and management adaptations to these physical 

4Historically, fungicide use in soybean production has been very low.

5A gene or genetic material transferred naturally or via genetic engineering from one organism to another.

6See http://www.aphis.usda.gov/

newsroom/2011/05/ea_corn.shtml and 

http://www.monsanto.com/products/

Pages/drought-tolerant-corn.aspx. 
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factors have the potential to change yield response. The impacts of increasing 

atmospheric CO2, increasing temperatures, and variable precipitation on pest/

disease incidence and temperature- and water stress-related yield impacts are 

both complex and uncertain. A number of public agricultural research projects have addressed crop tolerance to heat, drought, and pest/disease infestations, as reported in USDA’s Current Research Information System (CRIS) 

(table 17). 

Based on project counts from USDA’s CRIS, relatively little research has been 

done on crop adaptation to higher temperatures.7 A great deal of research has 

focused on pests and diseases and the potential for host plant resistance. 

Private-sector companies have invested a great deal of resources into 

GE crops, particularly for insect resistance or herbicide tolerance (HT). 

Genetically engineered HT, particularly to Roundup, is widely used in U.S.-

grown soybeans, corn, and cotton, and the bacterium Bacillus thuringiensis, 

or Bt, is used for insect resistance in corn and cotton. Genetically engineering 

crops for drought tolerance is still at various phases of research development, depending on the crop, but several companies do have conventionally 

bred drought-tolerant corn varieties on the market. The level of heat tolerance research by private seed-biotechnology companies is unknown, since at 

present they do not include this trait in reported research pipelines.

Impact of Adopting Drought-Tolerant Crop Varieties

Drought-tolerant crop varieties are a good example of an adaptive genetic 

response to climate change with likely impact by 2030. We assumed patterns 

for the potential impact of drought-tolerant varieties for four major crops on 

nonirrigated land: 

• Corn: Drought-tolerant varieties increased yields on nonirrigated land 

by 15 percent in all areas where annual precipitation was less than 700 

millimeters (mm) and nonirrigated yields in 2010 were less than 80 

bushels per acre (bu/acre).

7Total fi nancial resources devoted 

to each project were not, in general, 

publicly available.

Table 17

U.S. public sector agricultural research projects, by crop stressor, as 

reported in 2010*

Stress

Projects that focus 

on crop stressor

Projects that partially 

focus on crop stressor

Number (percent)

Heat 17

(3.7)

59

(5.8)

Drought 49

(10.7)

149

(14.7)

Pests and diseases 390

(85.5)

808

(79.5)

Total 456

(100)

1,016

(100)

*Ninety-fi ve percent of these projects were initiated during 2001-10.

Source: USDA, Current Research Information System (CRIS) database, November 2010.
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• Wheat: Drought-tolerant varieties increased yields on nonirrigated land 

by 10 percent in all areas where annual precipitation was less than 625 

mm and nonirrigated yields in 2010 were less than 50 bu/acre.

• Soybeans: Drought-tolerant varieties increased yields on nonirrigated 

land by 10 percent in all areas where annual precipitation was less than 

700 mm.

• Cotton: Drought-tolerant varieties increased yields on nonirrigated land 

by 10 percent in all areas where annual precipitation was less than 900 mm.

The relative yield increases for wheat, soybeans, and cotton were assumed to 

be less than that for corn, since a drought-tolerant trait will soon be deployed 

in corn. Drought-tolerant varieties of wheat, soybeans, and cotton are in 

earlier stages of development. Also, no advances in drought tolerance were 

assumed for crops other than these four. Yield increase assumptions for 

drought-tolerant corn varieties were based on current experimental results 

reported by Castiglioni et al. (2008). In the absence of similar experimental 

results for wheat, soybeans, and cotton, lower percentage yield increases were 

assumed for REAP regions where lower yields and precipitation totals were 

both operative. Figure 24 shows the regions where the drought-tolerant varieties are planted in the scenario analyzed.
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Several factors suggest that these yield increments might be conservative if 

improved drought tolerance is commercialized within the next few years and 

then widely adopted. First, additional research might enhance potential yields 

beyond 10-15 percent in low-moisture environments. Second, depending on 

how well drought-tolerant varieties perform relative to other varieties under 

better moisture conditions, some drought-tolerance traits might be introduced 

into varieties for higher rainfall areas, as insurance against less favorable 

production conditions in some years. The threshold assumption currently 

defi ning water-stressed regions concerns annual precipitation; a more sophisticated analysis would consider precipitation and soil moisture conditions 

during crucial periods of the growing cycle. 

Our analysis compared variables of interest, such as crop area and crop 

prices, between scenarios in which farmers had adapted crops, rotations, and 

production practices with the availability of drought-tolerant varieties and 

scenarios in which they had made these adaptations without drought-tolerant 

varieties.8 Tables 18 and 19 indicate aggregate acreage and price changes 

within each climate change scenario. In each instance, estimates from the 

introduction of drought-tolerant varieties were compared with results without 

drought-tolerant varieties within the context of that particular climate change 

8In other words, the starting points 

for the analysis were the adaptation 

scenarios from earlier sections of the 

report. Drought-tolerant varieties were 

added as an additional adaptation option for farmers.

Table 18

U.S. acreage change if drought-tolerant varieties are available 

relative to adaptation case with current varieties

Crop ECH CSIRO CNR MIROC

Million acres

Total -0.5 -1.3 -1.2 -1.4

Corn 0.5 0.1 0.2 0.1

Wheat -1.1 -1.3 -1.3 -1.1

Soybeans 0.4 0.2 0.1 -0.2

Cotton 0.0 0.1 0.0 0.0

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Table 19

Crop price impacts of drought-tolerant varieties compared 

with baseline prices

Crop Adaptation ECH CSIRO CNR MIROC

Percent change 

Corn Normal -2.2 -2.1 3.7 6.1

DT -3.0 -2.9 2.8 5.3

Soybeans 

Normal -3.5 0.3 7.6 22.1

DT -3.6 0.1 7.4 21.9

Wheat

Normal -1.5 -5.9 -0.8 -1.0

DT -4.3 -9.4 -4.0 -4.3

Cotton

Normal -19.8 -14.6 -17.8 -22.8

DT -20.6 -15.6 -18.7 -23.6

DT = Drought-tolerant corn, soybeans, wheat, and cotton planted in some regions. 

Normal = No drought-tolerant varieties planted.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models
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scenario. Including drought-tolerant varieties reduced total planted acreage 

across all the scenarios. Corn acreage increased in all scenarios, as did 

soybeans, except for in the MIROC scenario; wheat acreage decreased in all 

scenarios due to improved productivity in major wheat-producing regions 

(table 18). The change in acreage was less sensitive to the climate scenario 

than in other cases examined. 

The introduction of drought-tolerant varieties reduced prices for corn, 

soybeans, wheat and cotton in all scenarios, with wheat showing the biggest 

reduction (table 19). Prices changed for these crops in the same direction 

from the baseline in all scenarios; prices that went up in the adaptation case 

went up less with drought tolerance and prices that declined were further 

reduced with the adoption of drought-tolerant varieties. Price changes for 

other crops varied by crop and scenario.

Net returns for all scenarios (table 20) increased for the United States as a 

whole compared with the adaptation scenario without drought-tolerant varieties. The Northern and Southern Plains regions benefi ted from the introduction of drought-tolerant varieties, but returns were reduced in the Corn 

Belt, which does not plant drought-tolerant varieties under any scenario. 

The change in returns was consistent across climate change scenarios, 

indicating that drought tolerance could be benefi cial under a wide range of 

adverse climate changes.

Impacts on acreage from the introduction of drought-tolerant varieties were 

more distinct at the regional level than at the national aggregate level. The 

only region that showed an increase in total acreage under all scenarios was 

the Northern Plains. Acreage moved from other western regions; regions east 

of the Mississippi River showed little change in acreage (fi g. 25).

The impacts of introducing drought-tolerant varieties were most apparent 

when examining crop production by region. With a few exceptions, corn, 

wheat, and soybean production shifted from the eastern half of the country to 

the drier Plains and Mountain regions. These changes were seen only at the 

margin; the Corn Belt region, for example, remained the largest producer of 

Table 20

Change in annual net returns to crop production from 2030 climate 

change scenarios with introduction of drought-tolerant varieties

Region ECH CSIRO CNR MIROC

$ Million

Corn Belt -369 -374 -391 -327

Delta -36 -31 -27 -31

Lake States -86 -91 -97 -84

Northern Plains 479 638 610 684

Southern Plains 273 307 275 296

Other U.S. regions 146 89 158 115

U.S. total 372 506 487 623

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Results describe the change relative to the 2030 climate change scenario without the addition 

of drought-tolerant varieties.
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corn and soybeans. The addition of drought-tolerant varieties does not lead 

to signifi cant corn production redistribution, even under the more extreme 

scenarios (fi g. 26); corn is not a major crop in the regions where droughttolerant varieties are planted, and the yield benefi t in those regions is not 

suffi cient to allow corn to compete signifi cantly with other crops, or other 

regions. Wheat production increased in the Northern Plains with the introduction of drought-tolerant varieties, but decreased in the Southern Plains as 

a result of declining prices (fi g. 27). Soybean production increased slightly 

in the Northern Plains and Southern Plains with the introduction of droughttolerant varieties (fi g. 28).

One environmental variable—nitrogen loss to water—showed little change 

at the national level. Nitrogen loss improved in the Northern Plains, even as 

total acreage increased (fi g. 29). Production in the region moved from continuous to multi-crop rotations, which tend to have lower nutrient loss. Slightly 

reduced acreage led to a decrease in nitrogen lost to water in the eastern 

regions. Acreage in the Southern Plains increased in crop rotations that had 

higher nitrogen losses, leading to increased overall nitrogen losses for the 

region. The pattern of regional change in sheet and rill erosion is similar to 

that of nitrogen losses to water.

Figure 25

Regional change in total planted acres from 2030 climate change scenarios with introduction 

of drought-tolerant varieties

Results describe the change relative to the 2030 climate change scenario without the addition of drought tolerant varieties.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

2.0% to 3.6% 0.4% to 2.0% -1.2% to 0.4% -2.8% to -1.2% -4.4% to -2.8% -6.0% to -4.4% -7.6% to -6.0%

ECH

CSIRO MIROC

CNR
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Figure 26

Regional change in corn production from 2030 climate change scenarios with introduction

of drought-tolerant varieties

Results describe the change relative to the 2030 climate change scenario without the addition of drought tolerant varieties.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

10.4% to 16.4% 4.5% to 10.4% -1.4% to 4.5% -7.3% to -1.4% -13.3% to -7.3% -19.2% to -13.3% -25.1% to -19.2%

ECH

CSIRO MIROC

CNR
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Figure 27

Regional change in wheat production from 2030 climate change scenarios with introduction 

of drought-tolerant varieties

Results describe the change relative to the 2030 climate change scenario without the addition of drought tolerant varieties.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

13.1% to 17.7% 8.5% to 13.1% 3.9% to 8.5% -0.7% to 3.9% -5.3% to -0.7% -9.9% to -5.3% -14.6% to -9.9%

ECH

CSIRO MIROC

CNR
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Figure 28

Regional change in soybean production from 2030 climate change scenarios with introduction 

of drought-tolerant varieties

Note: Soybeans not cultivated in the Pacific and Mountain regions.

Results describe the change relative to the 2030 climate change scenario without the addition of drought tolerant varieties.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

4.4% to 5.2% 3.5% to 4.4% 2.7% to 3.5% 1.9% to 2.7% 1.0% to 1.9% 0.2% to 1.0% -0.7% to 0.2%

ECH

CSIRO MIROC

CNR
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Figure 29

Regional change in nitrogen loss to water from 2030 climate change scenarios with introduction 

of drought-tolerant varieties

Results describe the change relative to the 2030 climate change scenario without the addition of drought tolerant varieties.

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations.

ECH

CSIRO MIROC

CNR

-2.2% to -1.5% -1.5% to -0.9% -0.9% to -0.2% -0.2% to 0.4% 0.4% to 1.1% 1.1% to 1.8% 1.8% to 2.4%
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Conclusions

The biophysical impacts from climate change on crop growth are both 

complex and uncertain. Although temperature increases associated with 

climate change are widely expected to lower crop yields, shifting regional 

precipitation patterns may either increase or decrease yields. There has also 

been considerable debate over the likely impacts of increasing atmospheric 

CO2 concentrations on plant growth. Our analysis suggests that the possible 

negative implications of climate-induced yield effects on crop prices, farm 

revenue, and food supply can be mitigated somewhat by farmers’ ability to 

adapt to changing climatic conditions through crop production decisions, 

technologies used, and the regional allocation of land. There also may be 

increased environmental impacts associated with expansion of cropland. 

Changes in pest distribution exacerbate production and revenue losses.

Nationally, acreage in crop production was fairly robust to climate change in 

the sense that aggregate acreage changes across scenarios (compared with 

the baseline projection) tended to be relatively small (less than 1 percent), 

whereas the range for individual regions was typically greater than the 

national range. This fi nding refl ects the fl exibility of the U.S. farm sector to 

respond to changing resource and market forces, resulting in production reallocations that minimize the aggregate disturbance to commodity supply and 

demand. In this analysis, available adaptation strategies included changing 

crops, crop rotations, and tillage types, as well as expansion or contraction 

of crop production acreage. Without additional pest impacts, mild climate 

change led to a reduction in crop prices; more extreme climate change 

resulted in price increases in some crops, most notably for soybeans and corn. 

Adaptation led to an increase in crop production, in general, though even with 

adaptation corn and soybean production declined under the more extreme 

climate projections. 

Total acreage in U.S. fi eld crop production increased in all climate change 

scenarios, although variations in yield changes across scenarios did not 

necessarily translate into acreage expansion in all regions. Corn and cotton 

were the only crops that increased acreage in all scenarios. Higher temperature and atmospheric carbon dioxide concentrations raised cotton yields 

considerably in every region where cotton was grown. The increase in supply, 

together with relatively elastic demand, caused cotton prices to drop precipitously, facilitating enough increase in demand to actually expand acreage 

even though existing acreage was already more productive. The corn yield 

effect was small relative to other crops across the scenarios, but high demand 

for corn raised corn acreage to compensate for lost yields. Corn acreage 

increased relatively more in regions where corn was not the predominant 

crop. Crop distributions in the Corn Belt and the Northern regions, in general, 

were less sensitive to climate change than in the Southern regions.

Price and production changes contributed to shifting regional returns to 

crop production. Returns in the Corn Belt, where much of the Nation’s fi eld 

crop production is concentrated, declined under all climate change scenarios 

in proportion to the severity of projected change. Farm returns nationally, 

however, increased under the two milder climate projections, with losses 

in the Corn Belt compensated for by increased returns in other regions as a 
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result of changes in price and production patterns. Our fi ndings suggest that, 

for regions outside the Corn Belt, changes in crop returns did not necessarily 

correspond to the general magnitude of the temperature and precipitation 

change of the scenario. Changes in relative productivity both across crops and 

within crops across regions, together with the resulting market-mediated price 

impacts, appeared to be the primary determinant of how any given region 

fared under changing climate conditions.

Extending the climate impact analysis to consider projected increases in 

pest-related yield and associated control costs showed that such impacts led 

to expanded planted acreage in all crops except for soybeans. Increases in 

pest prevalence reduced yields across all crops (except cotton and rice), but 

the differentiation of impacts by region and crop led to regional differences 

in acreage response. Corn production expanded in all regions except the 

Corn Belt, while wheat became relatively more important in the Southeast 

and soybeans shifted into the Northern and Southern Plains. Increased pest 

management costs meant a decline in farmer returns, though higher crop 

prices under the extreme climate scenarios partially offset increased control 

costs, with a greater impact on farmer returns in the hottest, driest scenarios. 

Since prices go up, producer losses are not offset by consumer gains, 

implying that consumers would be worse off.

Research is underway to generate adaptation options for climate change by 

introducing crop varieties that are tolerant to environmental stresses such as 

drought and high temperatures. Such developments will depend on technological opportunities, as well as on investments in research over time. We 

modeled one particular example—the introduction of drought-tolerant varieties of corn, wheat, soybeans, and cotton—that resulted in 10- to 15-percent 

higher yields in drier but nonirrigated environments. Within each climate 

change scenario, introduction of drought-tolerant varieties resulted in a slight 

reduction in crop prices. With a few exceptions, drought-tolerant varieties led 

to small shifts in corn, wheat, and soybean acreages from the eastern half of 

the United States into the Plains or Mountain States, where drought-tolerant 

varieties were introduced. In contrast to the additional pest-pressure case, 

lower producer returns in many regions were complemented by lower prices, 

benefi ting consumers.

Under most scenarios, changes in environmental indicators of soil and 

water quality generally followed acreage changes. Environmental impacts 

were also sensitive, however, to changes in production practices. Under 

several scenarios, the shift in nitrogen loss to water was proportionately 

larger than the total increase in cropped acreage, indicating an increase in 

production intensity for regions where more severe environmental impacts 

were observed, although soil loss to water erosion was commensurate with 

acreage change.

The results for the pest-pressure and drought-tolerance cases have been 

presented as changes relative to the climate change adaptation scenarios, 

rather than as changes from the baseline. This was done to show that the 

range of climate projections had an infl uence in determining how factors, 

such as biotic impacts (i.e., pests) and technological advancements, will 

ultimately affect U.S crop production patterns and the environmental consequences associated with them. An alternative way to consider the results 
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is to compare the scenario results for each case directly to the “no climate 

change baseline.” Rather than capturing the contribution of differences in 

crop yields between climate change scenarios, comparison with the baseline provides a measure of the composite effects of climate change plus the 

changes in production environment introduced in each case. Table 21 illustrates the changes in prices and returns from the climate change scenarios 

with respect to the “no climate change baseline.” The table indicates a consistent pattern corresponding to the climate change scenarios. In all cases, the 

ECH and CSIRO scenarios led to lower aggregate crop prices than under the 

“no climate change baseline,” whereas the more extreme CNR and MIROC 

scenarios led to higher aggregate crop prices. Similarly, crop returns followed 

the same trend. In the more extreme scenarios (CNR and MIROC), higher 

crop prices were generally paired with lower returns (compared with the “no 

climate change” baseline). Changes in relative profi tability within and across 

regions resulted in lower returns in the Corn Belt across all scenarios in the 

adaptation, pest-presssure, and drought-tolerance cases.

Environmental consequences generally increased across all scenarios for 

all cases (table 22). Increases in the environmental measures were largely 

the result of an expansion in total planted acres under the climate change 

scenarios, with some contribution from redistribution of crop production 

between regions with different soil and water characteristics. For nitrogen 

loss to water, the percent change in the indicator value was generally greater 

than the percent change in acreage, suggesting both intensifi cation of agricultural production through increased fertilizer use and redistribution of 

crop production to regions and rotations more susceptible to nutrient losses. 

In aggregate, changes in soil lost to water erosion were more moderate than 

those for nitrogen loss, with the percentage change in erosion impacts often 

less than the change in acreage overall. Regional erosion impacts varied 

widely, however.

Analysis Limitations and Future Research

In exploring the implications of climate change for U.S. fi eld crop production, 

our analysis focused on the yield-related impacts associated with increased 

regional average temperatures, varied regional changes in average precipitation, increased carbon dioxide concentration in the atmosphere, the expanded 

incidence of pests, and the market-mediated price impacts that arise as a 

result of decentralized impacts and adaptive responses. The GCM results 

used to drive our climate change projections did not allow us to estimate 

changes in the variability of daily temperature and precipitation, but other 

studies suggest that changes in volatility of weather parameters—including 

increased incidence of extreme weather events—may also be a signifi cant 

driver of yield and impact changes (Isik and Devadoss, 2006). By capturing 

only changes in average maximum daily temperature, minimum daily 

temperature, and precipitation, our EPIC results may underestimate the full 

yield impacts expected from changes in those climate conditions. 

Furthermore, direct yield impacts represent only a partial, if important, 

subset of the climate change elements likely to impact farmers. Other atmospheric factors—such as ground-level ozone, which is expected to increase 

in tandem with increasing CO2 emissions—may have signifi cant agricultural 
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Table 21

Economic effects of climate change scenarios on agriculture: 

Changes from “no climate change” baseline

Changes in crop prices 

(aggregated)

REAP Cases ECH CSIRO CNR MIROC

Percent

No adaptation -2.1 -0.9 3.9 8.3

Adaptation -4.0 -2.9 1.9 6.4

Pest damage -1.5 -0.8 5.1 11.9

Drought-tolerance -4.7 -3.6 1.1 5.6

Changes in annual net returns to crop production 

(United States)

ECH CSIRO CNR MIROC

$ Million 

No adaptation 4,185 2,664 493 -6

Adaptation 3,619 2,165 -332 -1,465

Pest damage 1,716 694 -2,936 -4,473

Drought-tolerance 3,992 2,671 155 -843

Changes in annual net returns to crop production

 (Corn Belt)

ECH CSIRO CNR MIROC

$ Million 

No adaptation 299 -680 -747 -2,592

Adaptation -1,114 -2,165 -2,112 -4,053

Pest damage -1,576 -2,608 -2,680 -4,564

Drought-tolerance -1,482 -2,538 -2,503 -4,381

Changes in annual net returns to crop production

(all regions except Corn Belt)

ECH CSIRO CNR MIROC

$ Million 

No adaptation 3,886 3,344 1,240 2,586

Adaptation 4,733 4,330 1,780 2,588

Pest damage 3,292 3,303 -256 91

Drought-tolerance 5,474 5,210 2,658 3,538

Notes: ECH, CSIRO, CNR, and MIROC are climate change scenarios (see table 4 for 

sources). The “no climate change” ““...baseline crop returns in REAP were $43 billion. 

The “no adaptation” case assumes climate change affects crop yield but that farmers’ land 

allocations and cropping practices do not adjust. The “adaptation” case allows farmers 

to adjust their crop rotations, tillage practices, and land allocation decisions in response 

to how the climate change affects crop yield and resulting market conditions. The “pest 

damage” case considers the additional effects of changes in the distribution of crop pests 

resulting from climate change and how this affects crop yields and pest control costs. 

The “drought-tolerance” case assumes that drought-tolerant crop varieties of corn, wheat, 

soybeans, and cotton become available and provide another option for how farmers can 

adapt to climate change. It does not include damages from changes in pest populations. 

See table 2 for a complete discussion of the REAP analysis cases.
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Table 22

Environmental effects of climate change scenarios on agriculture: 

Changes from “no climate change baseline”

Changes in total crop acreage

REAP Cases ECH CSIRO CNR MIROC

Million acres

No adaptation ----

Adaptation 1.9 1.8 0.8 3.2

Pest damage 4.0 2.7 5.0 8.8

Drought-tolerance 1.4 0.6 -0.3 1.9

Changes in total crop acreage

ECH CSIRO CNR MIROC

Percent

No adaptation ----

Adaptation 0.6 0.6 0.2 1.0

Pest damage 1.3 0.9 1.6 2.8

Drought-tolerance 0.5 0.2 -0.1 0.6

Changes in nitrogen losses to water

ECH CSIRO CNR MIROC

Percent

No adaptation -2.7 -2.4 0.4 1.4

Adaptation 1.4 1.5 2.1 5.0

Pest damage 5.7 5.0 5.2 6.8

Drought-tolerance 1.5 1.4 2.0 4.7

Changes in sheet and rill erosion

ECH CSIRO CNR MIROC

Percent

No adaptation -6.0 -3.5 -3.2 -0.2

Adaptation -0.9 0.6 0.9 1.2

Pest damage -0.8 1.0 3.3 4.9

Drought-tolerance -1.6 0.1 0 0.3

Notes: ECH, CSIRO, CNR, and MIROC are climate change scenarios (see table 4 for 

sources). The “no adaptation” case assumes climate change affects crop yield but that 

farmers’ land allocations and cropping practices do not adjust. The “adaptation” case allows farmers to adjust their crop rotations, tillage practices, and land allocation decisions in 

response to how the climate change affects crop yield and resulting market conditions. The 

“pest damage” case considers the additional effects of changes in the distribution of crop 

pests resulting from climate change and how this affects crop yields and pest control costs. 

The “drought-tolerance” case assumes that drought-tolerant crop varieties of corn, wheat, 

soybeans, and cotton become available and provide another option for how farmers can 

adapt to climate change. It does not include damages from changes in pest populations. 

See Table 2 for a complete discussion of the REAP analysis cases.
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impacts and may infl uence the incentives and constraints that farmers face in 

responding to a changing climate. The potential impacts of climate change 

on the supply and costs of agricultural inputs—such as land, energy, fertilizer, water, and labor—would also affect relative returns to different types 

of production and would possibly create region-specifi c constraints on the 

adaptive strategies available to farmers. Such considerations were beyond the 

scope of this analysis. Moreover, our research focuses on the potential for 

adaptation within the U.S crop sector, with a particular emphasis on major 

fi eld crops. We did not consider specialty crops—fruits, vegetables, nursery 

crops, and other specialty crops—which account for an important share of 

the value of U.S. agricultural production. We recognize, however, that climate 

change can have an important bearing on resources supporting specialty crop 

production as well. The model did consider livestock sector impacts through 

changes in feedgrain markets, although we did not capture the full range of 

substitution of feed ingredients that may occur due to climate change. Nor did 

we explicitly model the effects of climate change on animal productivity and 

other aspects of livestock management costs.

Our analysis also was limited in the range of adaptation strategies available 

to farmers that could be examined within the existing modeling framework. 

REAP evaluates adaptation strategies related to changing crop patterns and 

practices, but existing production enterprises in the model did not allow 

for other farm-level adaptation strategies, such as changing harvesting and 

planting dates or the timing and magnitude of applied irrigation or fertilizer. 

Such strategies are common responses to weather variability and will be an 

important element in farmers’ adaptation responses. The potential for, and 

constraints to, expanding irrigated acreage and water use may be a particularly signifi cant factor in adaptation strategies where there is already signifi -

cant competition for water resources, such as in the Western United States. 

REAP does not currently allow for an analysis of shifting irrigation patterns 

within U.S. agriculture; however, such modifi cations are underway and will 

inform ongoing ERS research related to climate adaptation.
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Appendix A: The REAP Model

The Regional Environment and Agriculture Programming (REAP) model is 

a static, partial equilibrium optimization model of the agricultural sector that 

quantifi es agricultural production and its associated environmental outcomes 

for 48 regions in the United States (see fi g. 1). The regions are defi ned by the 

intersection of USDA’s Farm Production Regions (defi ned by State boundaries) 

and Land Resource Regions (defi ned by predominant soil type and geography). 

REAP employs survey data (from USDA’s Agricultural Resource Management 

Survey (ARMS)) and simulated input data (from the Environmental Productivity 

and Integrated Climate (EPIC) model) at the regional level on crop yields, input 

requirements, costs and returns, and environmental parameters to estimate longrun equilibrium outcomes. Regional production levels are determined for 10 

crops and 13 livestock categories, and national production levels are determined 

for 20 processed products. For each REAP region, land use, crop mix, and 

acreage allocations by multi-year crop rotation and tillage practice are endogenously determined by REAP’s constrained optimization process. Input use and 

national-level prices are also determined endogenously. The model has been 

applied to address a wide range of agri-environmental issues, such as soil conservation and environmental policy design, environmental credit trading, climate 

change mitigation policy, and regional effects of trade agreements.

REAP is implemented as a nonlinear mathematical program using the 

General Algebraic Modeling System (GAMS) programming environment. 

The model determines a welfare-maximizing set of crop, livestock, and 

processed product production levels subject to land constraints and processing 

and production balance requirements. Production activities for crops within 

a region (defi ned by crop rotation and tillage) are allocated in the model 

solution based on a constant elasticity of transformation (CET) relationship. 

The CET specifi cation helps to avoids unrealistic “corner point” solutions 

by accounting for cost/return and risk considerations embedded in observed 

acreage allocations but not explicitly included in the model. The model is 

calibrated to USDA baseline production levels over a multi-year timeframe 

using the Positive Mathematical Programming (PMP) method. 

Production “shocks” under policy, technical, or environmental scenarios 

can be introduced via changes or additions to constraints, modifi cations 

of baseline data assumptions, adjustments in objective function terms, or 

some combination of approaches. Changes in policy, commodity demand, 

or production/processing technology can be imposed on the model and the 

results examined to determine their effects on: 

• Regional supply of crops and livestock; 

• Commodity prices; 

• Crop management and production input use; 

• Farm income; and 

• Environmental indicators, such as nutrient and pesticide runoff, soil loss, 

GHG emissions, soil carbon fl uxes, and energy use. 

For more information on REAP and its applications, see model documentation at http://www.ers.usda.gov/publications/tb1916/tb1916fm.pdf.
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Appendix B: Climate Projections for 2030

Using Climate Projections 

in Agro-Economic Analysis

Interest in climate change and weather processes, in general, has resulted in 

a wide array of General Circulation Models (GCMs) that attempt to project 

climate trends. GCMs are highly complex models that use assumptions about 

future emissions paths and the evolution of atmospheric concentration of 

various greenhouse gases (GHGs) to estimate spatial patterns of temperature 

and precipitation. Because of their numerical complexity, GCMs simulate 

climate conditions at a very coarse spatial and temporal resolution. They 

simulate how atmospheric behavior that is averaged over relatively long 

periods of time (climate) will change, but they do not model weather or localized atmospheric conditions, such as storm events, drought, or temperature 

extremes over short periods of time. Also, the spatial resolution of GCMs is 

not suffi cient to capture the wide variation in temperature, precipitation, and 

other meaningful weather conditions infl uenced by detailed ground topography that, in turn, infl uence crop production. A grid cell in a GCM may 

cover approximately 10,000 square kilometers, resulting in an average of only 

15 cells per REAP region.

Downscaling

Spatial downscaling is a process that provides fi ner resolution climate information from the lower resolution data emerging from GCMs. Downscaling 

methods generally apply assumptions about regional climate dynamics to 

disaggregate the GCM averages down into region-specifi c forecasts. One of 

the major assumptions of spatial downscaling is that it is possible to determine signifi cant relationships between local and large-scale climate that will 

remain valid under future climate conditions. There are several methods, 

often involving simulation or statistics, to spatially downscale GCM output; 

the method used to generate the data used in this report is described in Jones 

et al., 2009.

Choice of Models and Data

For this analysis, we focused on the SRES A1B emissions scenario. To 

choose GCM outputs, we considered whether the outputs:

• Were downscaled by the same method to the same resolution across our 

entire U.S. study area;

• Included the appropriate set of climate variables for the EPIC cropgrowth simulator analysis—monthly minimum temperature and 

maximum temperature and precipitation; and

• Covered the 2030 projection year used in the REAP analysis, using an 

average of weather projections over a period of at least 5 years.

The downscaled GCM output we employed was generated by Jones et al. 

(2009). The four models and their sources are listed in table 4. The data 

included the following disclaimer, “These downscaled climate data are NOT 
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predictions of what the future climate will be like in any place. They are 

projections of possible future climate, and should be treated with considerable 

caution. There is a great deal of variability between different climate models, 

between different greenhouse gas emission scenarios, and between different 

downscaling methods.”

Aggregating Climate Data to REAP Regions

The downscaled climate data covered the coterminous United States. Most of 

the points corresponded to land unsuitable to agriculture, such as mountain 

ranges, urban areas, or bodies of water. Much of the United States is rangeland that is not in use and is unlikely to be used in the near future for crop 

agriculture. Since we required a single average weather value representative 

of each region to derive crop yield estimates, it was important that the values 

refl ected conditions that were representative of conditions in agricultural 

areas. Nonagricultural data points can bias weather characteristics and alter 

the performance of the crop-growth simulation. The downscaled weather data 

were confi ned to agricultural production areas by overlaying National Land 

Cover Database (NLCD) results for cultivated crops and pasture/hay categories onto the REAP regions (Homer et al., 2004). Average monthly values 

for maximum temperature and precipitation and minimum temperature were 

computed from agricultural land within each REAP region (see fi g. 1 for a 

map of the REAP regions).
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Appendix C: Sensitivity of Analysis Results 

to Climate Change Elements

The Environmental Productivity and Integrated Climate (EPIC) simulation of the yield impacts of simultaneously changing values of temperature, 

precipitation, and carbon dioxide concentration drives REAP’s analysis of 

the impacts of future climate scenarios relative to a baseline scenario. EPIC’s 

results are in turn driven by a large set of technical parameter assumptions that are held constant across climate scenarios but that, through their 

infl uence on the relative impact of temperature, precipitation, and carbon 

dioxide concentration on crop yields, can subsequently infl uence differences 

in impact across future climate scenarios. Examples of such assumptions 

include the minimal and optimal growth temperatures for each crop, the 

parameters of the relationship between carbon dioxide concentration and crop 

growth, water-related parameters, such as maximum stomatal conductance, 

and assumptions about the rate of decline in radiation use effi ciency with 

increasing vapor pressure defi cits.1

Because there is ongoing debate about the expected magnitude of impacts 

from factors such as carbon dioxide concentration (i.e. carbon dioxide 

fertilization), and to understand how each element of the climate change 

impact behaves individually in EPIC’s results, it is helpful to present 

disaggregated climate change impact results. EPIC scenarios in which 

temperature, precipitation, and carbon dioxide concentrations are varied 

independently of one another are described in appendix table 1. Note that 

because of interaction effects, the impact of the combined changes is not a 

strict sum of the impact of individual effects. Carbon dioxide fertilization’s 

impact on transpiration, for example, could alter the sensitivity of impact 

results to precipitation changes.

To isolate the biophysical impacts from the behavioral impacts in this analysis, results are presented for a series of “Impact without Adaptation” cases 

where production acreage is fi xed across all scenarios (Appendix fi g. 1). 

Productivity changes are due exclusively to changes in biophysical impact. 

As mentioned in the section on “Climate Impact Analysis: No Adaptation,” 

regional yield changes refl ect yield changes at the crop rotation level (as 

measured by EPIC) that are then weighted by rotation acreage in aggregating 

up to the regional level. 

1For a complete list of EPIC’s 

parameters, see the EPIC documentation at http://epicapex.brc.tamus.edu/

media/23015/epic0509usermanualupdated.pdf.

Appendix table 1

Climate scenarios used to explore yield impact sensitivity to climate change elements

Measure Baseline CNR CNR_No_

CO2

CNR_JUST_

CO2

CNR_Base_

T = CNR

CNR_Base_

P = CNR

Temperature (year) 2000 2030 2030 2000 2000 2030

Precipitation (year) 2000 2030 2030 2000 2030 2000

Carbon dioxide concentration (ppm) 381 450 381 450 450 450

CNR = Centre National de Recherches climate scenario.

CNR_No_CO2 = CNR temperature and precipitation, baseline CO2 concentration.

CNR_JUST_CO2 = Baseline temperature and precipitation, elevated CO2 concentration.

CNR_Base_T = CNR climate scenario precipitation and elevated CO2 concentration, with baseline temperature.

CNR_Base_P= CNR climate scenario temperature and elevated CO2 concentration, with baseline precipitation.

Ppm = Parts per million.
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Appendix fi gure 1 reveals the magnitude of carbon dioxide fertilization 

impact by crop, as well as the relative impact of temperature change versus 

precipitation change in the presence of carbon dioxide fertilization. As 

expected, carbon dioxide fertilization alone (CNR_JUST_CO2) resulted 

in crop productivity increases for all crops relative to the baseline case 

(appendix table 2). The aggregate numbers masked differences in carbon 

dioxide fertilization impacts across regions. Appendix table 3 illustrates those 

differences for corn and soybeans.

A comparison of CNR_T_Base_P and CNR_P_Base_T results illustrates the 

relative impact of temperature change versus precipitation change on biophysical 

impacts, when averaged nationwide and in the presence of the carbon dioxide 

fertilization effect. For some crops, the precipitation change associated with the 

CNR scenario actually increased productivity relative to carbon dioxide fertilization 

alone; that dynamic was seen for rice and silage, for instance. Silage, with concentrations of production in the Northeast and Lake States regions, benefi ted from the 

increased precipitation in those regions associated with the CNR projection.2

Applying the temperature changes associated with the CNR model 

(CNR_T_Base_P) almost always decreased productivity relative to the 

carbon dioxide fertilization only case (CNR_JUST_CO2). There was very 

little temperature impact on barley and hay, and cotton yields increased 

signifi cantly with the temperature increase. Cotton yield’s positive response 

to temperature change was not consistent with the literature and possibly 

was a result of a disparity between the optimal temperature for leaf and 

2Rice reacts counterintuitively; with 

production concentrated in the Delta 

region, rice thrives despite lower precipitation levels. Because rice is heavily irrigated, one could imagine little 

reaction to precipitation change, but a 

positive yield change seems an unlikely 

result. We are currently improving 

our treatment of rice irrigation and its 

interaction with REAP’s soil types for 

future analyses with a more sophisticated focus on water resources.

Appendix figure 1

U.S. average crop yields under various climate sensitivity scenarios

Average national crop yield

(units/acre)

CNR = Centre National de Recherches climate scenario.

CNR_No_CO2 = CNR temperature and precipitation, baseline CO2 concentration.

CNR_JUST_CO2 = Baseline temperature and precipitation, elevated CO2 concentration.

CNR_Base_T = CNR climate scenario precipitation and elevated CO2 concentration, 

with baseline temperature.

CNR_Base_P= CNR climate scenario temperature and elevated CO2 concentration, 

with baseline precipitation.

Cwt = Hundredweight.

Source: USDA, Economic Research Service calculations 

Units/acre for silage, cotton, and hay

0

20

40

60

80

100

120

140

160

180

200

0

2

4

6

8

10

12

14

16

Barley

(bushels)

Corn

(bushels)

Oats

(bushels)

Rice

(cwt)

Sorghum

(bushels)

Soybeans

(bushels)

Wheat

(bushels)

Silage

(tons)

Cotton

(480-lb bales)

Hay

(tons)

CNR

Base

CNR_T_Base_P

CNR_P_Base_T

CNR_Just_CO2

CNR_No_CO2
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vegetative growth (37o C) and the optimal temperature for cotton boll 

growth (25-26o C). The EPIC analysis did not capture a decline in harvest 

index at the higher temperature and therefore translated higher biomass 

production at higher temperatures into higher yields.

The aggregate crop productivity impacts at both the regional and national 

level are weighted averages of what is occurring at the fi eld scale for each 

of REAP’s production enterprises, so the magnitude of change is not necessarily representative of what happens for any single rotation. The results for 

corn production in the Corn Belt, for instance, are an average of what would 

happen to corn yields in a continuous corn rotation and in a corn/soybean 

rotation (among others). Because the yield impacts of any single element of 

climate change are dependent on other factors in the crop production system, 

particularly water and nutrient constraints, those impacts vary signifi cantly 

across production enterprises for the same crop within a single region. 

Appendix table 3

Regional differences in carbon dioxide fertilization effect for corn 

and soybeans

Region Corn yield Soybean yield

Percent change

Appalachia 1.5 9.7

Corn Belt 1.8 10.3

Delta 1.8 8.1

Lake States 5.3 9.1

Mountain 5.1 N/A

Northern Plains 4.4 12.1

Northeast 3.5 10.0

Pacifi c 3.4 N/A

Southeast 2.3 11.0

Southern Plains 3.0 11.0

N/A – Not applicable.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.

Appendix table 2

Change, by crop, in nationwide average yield as a result of carbon 

dioxide fertilization in the CNR scenario

Crop Percent yield change

Barley 8.1

Corn 2.9

Oats 6.9

Rice 5.3

Sorghum 4.7

Soybeans 10.0

Wheat 5.8

Silage 4.7

Cotton 9.7

Hay 15.7

CNR - Centre National de Recherches general circulation model.

Source: USDA, Economic Research Service calculations based on Regional Environment 

and Agriculture Programming (REAP) model data.
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Appendix D: An Econometric Model 

of Regional Pesticide Expenditures

Movement of Crop Production Regions 

in Latitude-Temperature Space

The premise of the pest impact analysis in our study was based on the assumption that rising temperatures with climate change will make production conditions—and potential pest infestations—more comparable with observed 

conditions farther south. Quadratic relationships between average annual 

temperature and latitude centroid for REAP crop production regions for 2000 

and 2030 were estimated using temperature projections from the ECH, CSIRO, 

CNR, and MIROC climate models applied to the Special Report on Emissions 

Scenarios (SRES) A1B emissions scenario. These relationships were used 

to estimate the effective southward percentage shift in each region’s latitude 

centroid in temperature space for each climate-model projection.

Average annual temperature was regressed on a constant latitude centroid, 

and latitude centroid squared for current weather conditions and for each 

model 2030 projection using ordinary least squares (OLS) (appendix table 

4). As shown, latitude accounted for 93 percent of the variation in average 

annual temperatures for each of the climate models. Additionally, coeffi cient 

estimates were statistically different from zero at the 1-, 5-, and 10-percent 

levels, except for the square of the latitude centroid in the CSIRO (p=0.195) 

and MIROC (p=0.116) climate models. 

The relationships between average annual temperature and latitude centroid 

are displayed graphically for the base year (2000) and for each climate 

model for 2030 in appendix fi gure 2. Except for the lower latitudes (below 

32°N), the largest increases in average annual temperature occurred using the 

MIROC temperature projections, and the lowest increases in average annual 

Appendix table 4

Ordinary least-squares estimates for the quadratic relationships between average annual temperature 

and REAP region latitude centroids

Variable Base yeara ECHb CSIROc CNRd MIROCe

Intercept 60.9399*** 61.097784*** 56.707154*** 61.776466*** 58.6598***

Latitude centroid -1.6851*** -1.623285*** -1.407831*** -1.631187*** -1.486675***

Latitude centroid2 0.0107* 0.009895 0.007144* 0.009859 0.008593***

*=Statistically different at the 1-percent level.

**=Statistically different at the 5-percent level.

***=Statistically different at the 10-percent level.

Notes: Average annual temperatures in each of the 48 REAP regions for a 2000 base year and 2030 temperature projections based on 

the ECH, CSIRO, CNR, and MIROC climate models applied to the SRES A1B emissions scenario were regressed on a vector of ones and

the REAP regions’ latitude centroids and latitude centroids squared. The least-squares estimates are reported in this table. The standard 

errors do not account for the use of temperature projections for 2030. Forty-eight observations were used in each regression.

aThe standard error of the estimate, σ, is 1.28, the coeffi cient of determination, R2, is 0.93, and the hypothesis that all coeffi cient estimates are zero can be rejected at the 1-percent level, F=320.63***. b σ=1.16, R2=0.95, and F=391.12***. c σ=1.20, R2=0.94, and F=361.97***. d σ=1.25, R2=0.94, and F=347.42***. e σ=1.18, R2=0.94, and F=344.70***.
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temperature occurred using the CSIRO temperature projections. Except for 

the lower latitudes, average annual temperatures for the CNR, CSIRO, and 

ECH climate projections were between base year levels and MIROC temperature projections. The largest increases in average annual temperature below 

roughly 29°N occurred using the CNR temperature projections.

The simulated southward movement of each REAP region’s latitude centroid 

implied by the OLS estimates is reported in appendix table 5. These fi gures 

represent the estimated southward shift of each region in latitude-temperature 

space. Empirical estimates that relate latitude to real (2006 US$) pesticide 

expenditures were applied to each region’s adjusted latitude to predict percent 

changes in expenditures and yield losses in 2030 relative to 2000.

Pesticide Expenditures and Latitude

Phase 2 Agricultural Resource Management Survey (ARMS) data collected 

at the fi eld level from barley (2003), corn (1996, 2001, 2005), cotton (1997, 

2003, 2007), oat (2005), rice (2000, 2007), sorghum (2003), soybean (1997, 

2002, 2006), and wheat (2004) producers were used to estimate a relationship 

between the natural log of pesticide expenditures per acre, a constant, a time 

index, regional fi xed effects, and a separate latitude coeffi cient for each crop.1

The time index was included to account for annual weather variation during 

1996-2007. National Agricultural Statistics Service (NASS) production-region 

fi xed effects (not REAP region fi xed effects) were included in the regression 

to account for spatial variation in weather and production possibilities.2 The 

dummy variable for the Corn Belt was excluded.3 Separate coeffi cient estimates were obtained for each crop by adding independent variables equal to the 

product of the fi eld’s latitude and the crop dummy variables.

1Pesticide expenditures, which 

include payments for herbicides, insecticides, and fungicides, were converted 

to real (2006 US$) expenditures using 

the Bureau of Labor Statistics (2010) 

producer price index for pesticides and 

other agricultural chemicals.

2In another version of the model, 

separate annual dummy variables were 

included for each NASS production 

region to account for annual weather 

variation by region and over time more 

completely. Because the adjusted coeffi cient of determination was slightly 

lower in that version of the model 

and because the estimation results 

were very similar, we used the results 

reported in appendix table 6 to specify 

the pesticide-expenditure and yieldloss impacts used in the analysis.

3Note that the REAP regions are 

subsets of the major production regions 

included in the regression.

Appendix figure 2

Quadratic relationships between average annual temperature 

and latitude centroid for the base year and for the climate models

 in 2030

Average annual temperature (degrees Celsius)

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models.

Source: USDA, Economic Research Service calculations 

Latitude

0

5

10

15

20

25

30

25 30 35 40 45 50

Base 2000

CNR 2030

CSIRO 2030

ECH 2030

MIROC 2030
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Agricultural Adaptation to a Changing Climate: Economic and Environmental Implications Vary by U.S. Region / ERR-136 

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Appendix table 5

REAP region southward shifts in latitude-temperature space

REAP

region

Latitude 

centroid

Climate Models

CNR CSIRO ECH MIROC

new latitude centroid

APN 37.13 35.31 35.83 35.65 34.66

APP 35.90 34.12 34.65 34.46 33.55

APS 38.77 36.90 37.42 37.23 36.13

APT 35.89 34.10 34.63 34.44 33.53

CBL 41.18 39.23 39.79 39.55 38.31

CBM 40.66 38.72 39.27 39.04 37.83

CBN 37.93 36.09 36.61 36.42 35.38

CBO 36.63 34.83 35.36 35.17 34.21

CBR 41.08 39.13 39.70 39.45 38.22

DLN 35.61 33.84 34.37 34.18 33.28

DLO 33.56 31.86 32.44 32.21 31.45

DLP 33.47 31.78 32.35 32.12 31.37

DLT 30.25 28.67 29.35 29.03 28.47

LAF 47.58 45.43 46.33 45.70 44.08

LAK 44.87 42.80 43.51 43.10 41.64

LAL 42.98 40.97 41.59 41.28 39.93

LAM 44.19 42.15 42.82 42.45 41.03

MNB 43.32 41.30 41.94 41.61 40.24

MND 39.34 37.44 37.97 37.77 36.64

MNE 45.62 43.53 44.28 43.82 42.31

MNF 48.37 46.21 47.18 46.48 44.81

MNG 42.24 40.25 40.84 40.56 39.26

MNH 36.48 34.68 35.21 35.02 34.07

NPF 46.78 44.65 45.49 44.94 43.36

NPG 43.17 41.16 41.78 41.46 40.10

NPH 39.36 37.47 38.00 37.80 36.67

NPM 41.55 39.58 40.16 39.90 38.64

NTL 42.99 40.98 41.60 41.28 39.93

NTN 40.50 38.56 39.11 38.89 37.69

NTR 42.78 40.77 41.39 41.08 39.75

NTS 40.11 38.19 38.74 38.52 37.34

NTT 38.79 36.91 37.44 37.24 36.15

PAA 45.07 43.00 43.72 43.30 41.82

PAB 46.54 44.42 45.23 44.70 43.14

PAC 36.95 35.13 35.66 35.47 34.49

PAD 39.65 37.74 38.28 38.07 36.92

PAE 48.31 46.14 47.10 46.41 44.74

SPD 31.09 29.48 30.12 29.83 29.22

SPH 34.41 32.69 33.24 33.03 32.21

SPI 28.41 26.88 27.64 27.24 26.81

SPJ 33.70 32.00 32.57 32.35 31.58

SPM 36.22 34.43 34.96 34.77 33.83

SPP 32.71 31.04 31.63 31.39 30.68

SPT 29.16 27.62 28.34 27.97 27.49

STN 34.49 32.76 33.31 33.10 32.28

STP 32.29 30.63 31.23 30.98 30.30

STT 32.92 31.24 31.83 31.59 30.87

STU 27.56 26.07 26.87 26.43 26.05

See table 4 for the sources of the CNR, CSIRO, ECH, and MIROC models. 

Note: Figure 4 displays the location of these regions.
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This econometric model was used to test the hypothesis that problems with 

agricultural pests are more severe in southern crop production regions than in 

northern production regions (Bridges, 1992). Our analysis provided empirical 

support for the hypothesis for barley, corn, oats, sorghum, soybeans, and 

wheat; empirical support against this hypothesis for cotton; and empirical 

support neither for nor against this hypothesis for rice. The maximum likelihood estimates (appendix table 6) indicate a negative and statistically signifi -

cant relationship between pesticide expenditure and latitude for barley, corn, 

oats, sorghum, soybeans, and wheat. The estimates indicate a positive and 

statistically signifi cant relationship for cotton and a positive and insignifi cant 

relationship for rice. The estimates suggest that pesticide expenditures in 

2030 will increase relative to 2006 for barley, corn, oats, sorghum, soybeans, 

and wheat; decline for cotton; and remain the same for rice.

Appendix table 6

Pesticide costs per acre versus latitude, by crop, maximum likelihood 

estimates for the groupwise heteroscedasticity model

dependent variable

observations 

standard error

Adjusted R2

F

Natural log of pesticide expenditures per acre

19,701

1.00

0.43

840.59***

Variable Coeffi cient Standard error

intercept 4.1915*** 0.0979

year -0.0386*** 0.0015

latitude·barley -0.0311*** 0.0023

latitude·corn -0.0186*** 0.0024

latitude·cotton 0.0066** 0.0026

latitude·oats -0.0460*** 0.0027

latitude·rice 0.0030 0.0027

latitude·sorghum -0.0250*** 0.0026

latitude·soybeans -0.0267*** 0.0025

latitude·wheat -0.0245*** 0.0024

Appalachia -0.1022*** 0.0208

Delta -0.0815 0.0250

Lake States -0.0295 0.0181

Mountain -0.2017*** 0.0311

Northern Plains -0.2066*** 0.0159

Northeast 0.0313 0.0312

Pacifi c 0.1845*** 0.0366

Southern Plains -0.7614*** 0.0301

Southeast -0.0744** 0.0328

*=Statistically signifi cant at the 1-percent level.

**=Statistically signifi cant at the 5-percent level.

***=Statistically signifi cant at the 10-percent level.

Notes: Phase 2 ARMS data for barley (2003), corn (1996, 2001, 2005), cotton (1997, 2003, 

2007), oats (2005), rice (2000, 2006), sorghum (2003), soybeans (1997, 2002, 2006), and 

wheat (2004) were used. Iterated weighted least squares was used assuming groupwise 

heteroscedasticity, where the groups were the different crops. Ten iterations were required. 

The null hypothesis of homoscedasticity was rejected (x=1418.12, df=7, p<0.0001