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CLIMATE

INDICATORS

AGRICULTURE

for
Climate Indicators for Agriculture

Tis report may be cited as:

Walsh, M. K., P. Backlund, L. Buja, A. DeGaetano, R. Melnick, L. Prokopy, E. Takle, D. Todey, L. Ziska. 2020. 

Climate Indicators for Agriculture. USDA Technical Bulletin 1953. Washington, DC. 70 pages. 

DOI https://doi.org/10.25675/10217/210930.

Production Design and Editorial Coordinator: Karen Slater, UCAR

Tis report is available online at 

https://www.usda.gov/sites/default/fles/documents/climate_indicators_for_agriculture.pdf

July 2020

Tis document was produced through a collaboration between the U.S. Department of Agriculture, Colorado 

State University, and the National Center for Atmospheric Research under an Interagency Agreement with the 

National Science Foundation and Cooperative Agreement #58-0111-18-015.

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USDA is an equal opportunity provider, employer, and lender.

i
TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION AND BACKGROUND: FRAMING THE INDICATORS ........... . 1

1.1 Report Scope, Goals, and Considerations ................................. . 3

1.2 Report Development ................................................... . 3

1.3 Report Organization and Use ............................................ . 5

1.4 Acknowledgments .................................................... . 6

CHAPTER 2 PHYSICAL INDICATORS ................................................ . 7

2.1 Extreme Precipitation .................................................. . 8

2.2 Soil Moisture ......................................................... . 10

2.3 Nighttime Air Temperature .............................................. . 12

2.4 Heat Waves .......................................................... . 14

2.5 Humidity ............................................................. . 16

CHAPTER 3 CROP AND LIVESTOCK INDICATORS ..................................... . 18

3.1 Animal Heat Stress .................................................... . 20

3.2 Crop-Growing Region Migration ......................................... . 22

3.3 Leaf Wetness Duration (LWD)............................................ . 24

CHAPTER 4 BIOLOGICAL INDICATORS .............................................. . 26

4.1 Weed Range and Infestation Intensity ..................................... . 27

4.2 Insect Infestation in Crops .............................................. . 29

4.3 Crop Pathogens ....................................................... . 31

4.4 Pesticide Use ........................................................ . 33

CHAPTER 5 PHENOLOGICAL INDICATORS........................................... . 35

5.1 Timing of Budbreak in Fruit Trees ........................................ . 37

5.2 Pollinators and Pollinator Management ................................... . 39

5.3 Winter Chill Units ...................................................... . 41

5.5 Disease Vectors in Livestock ............................................ . 45

CHAPTER 6 SOCIOECONOMIC INDICATORS ......................................... . 47

6.1 Crop Insurance Payments............................................... . 48

6.2 Total Factor Productivity (TFP) ........................................... . 50

6.3 Heat-Related Mortality of Agricultural Workers ............................. . 52

APPENDIX A REFERENCES......................................................... . 54

APPENDIX B REPORT AUTHORS AND AFFILIATIONS ................................... . 70

Climate Indicators for Agriculture

ii
Figure 1-1. Climate infuences on agricultural production and food system indicators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Figure 1-2 . Data availability and the strength of infuence of climate change on each indicator. . . . . . . . . . . . . . . . . . . . . . . 5

Figure 2-1. Increasing number of very heavy precipitation days in the central United States. . . . . . . . . . . . . . . . . . . . . . . . . 9

Figure 2-2. Soil moisture data for a location in Kentucky from the USDA SCAN network. . . . . . . . . . . . . . . . . . . . . . . . . . 11

Figure 2-3. Average annual number of nights with minimum temperatures greater than 

70°F, averaged over three decades across the southeastern United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 2-4. a) Annual average number of heat wave days (2 or more consecutive days with heat 

index >90oF) for 1989–2018. b) Trends in the number of annual heat wave days 

(1989-2019). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 2-5. U.S. summer dew point temperature trends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Figure 3-1. Trend in the July maximum one-day temperature-humidity index (THI) from 

1980 to 2018 derived from NOAA ASOS data for station KSTK at Sterling, Colorado. . . . . . . . . . . . . . . . . . . 21

Figure 3-2. Projected geographic distribution of changes in yield for rainfed corn by midcentury 

(a proxy for change in regions where corn production would be proftable). . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Figure 3-3. Tirty-one-day average of the July daily maximum values of relative 

humidity from 1980 to 2018 derived from NOAA ASOS data for station 

KSTK at Sterling, Colorado. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 4-1. Kudzu range in the midwestern United States, 1971–2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 4-2. Simulated changes in heat accumulation in the 21st century. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 4-3. Distance from the equator over time for fve major types of plant pathogens. . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Figure 4-4. Change in application of pesticide for genetically modifed soybean 

as a function of minimum temperatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Figure 5-1. Historical and projected changes in apple bud development stages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Figure 5-2. Te impact of April temperatures on bee emergence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Figure 5-3. Departure of annual chill unit accumulation from the 1961–1990 average 

at three locations in California’s Central Valley. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 5-4. Number of insect generations per season based 

on seasonal growing degree day accumulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Figure 5-5. Present and future (2050) geographic areas in the 

United States with suitable habitat for cattle tick. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Figure 6-2. Trends in total farm input, total farm output, and 

TFP (ratio of output to input) from 1948 through 2015. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Figure 6-3. Number and rate of heat-related deaths among crop workers

in the United States, by fve-year period, 1992–2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

LIST OF FIGURES Climate Indicators for Agriculture

iii
Climate Indicators for Agriculture

APHIS USDA Animal and Plant Health 

Inspection Service

ARS USDA Agricultural Research Service

ASOS Automated Surface Observing 

System

BLS U.S. Bureau of Labor Statistics 

Bt Bacillus thuringiensis 

CDC Centers for Disease Control and 

Prevention

CFOI Census of Fatal Occupational Injuries 

CFTEP Cattle Fever Tick Eradication Program 

CIMMYT International Maize and Wheat 

Improvement Center 

CO2 Carbon dioxide

CoCoRaHS Community Collaborative Rain Hail 

and Snow Network 

CPS U.S. Current Population Survey 

CRN U.S. Climate Reference Network 

CRS Congressional Research Service

DON DEOXYNIVALENOL also known as 

vomitotoxin

ERS USDA Economic Research Service

FAOSTAT Food and Agriculture Organization 

Corporate Statistical Database

GDD Growing Degree Days

GHCN Global Historical Climatology Network 

GRACE NASA’s Gravity Recovery and Climate 

Experiment 

IPCC Intergovernmental Panel on Climate 

Change

LWD Leaf wetness duration 

NASA National Aeronautics and Space 

Administration

NASS USDA National Agricultural Statistics 

Service 

NCA National Climate Assessment

NCEI NOAA National Centers for 

Environmental Information 

NEWA Network for Environment and Weather 

Applications 

NEXRAD Next Generation Weather Radar

NH3 Ammonia

NIFA USDA National Institute of Food and 

Agriculture

NOAA National Oceanic and Atmospheric 

Administration

NPDN National Plant Disease Diagnostic 

Network

NRCS USDA Natural Resources 

Conservation Service

NWS National Weather Service

NWS COOP National Weather Service Cooperative 

Observer Program

RMA USDA Risk Management Agency 

SCAN USDA Soil Climate Analysis Network 

SMAP Soil Moisture Active Passive 

SMOPS NOAA Soil Moisture Products System 

SO2 Sulfur dioxide

SPEI Standardized precipitation 

evapotranspiration index 

SPI Standardized precipitation index 

TFP Total factor productivity 

U.S. EPA U.S. Environmental Protection Agency

USDA U.S. Department of Agriculture

USGCRP U.S. Global Change Research 

Program

USGS U.S. Geological Survey

USWBSI U.S. Wheat and Barley Scab Initiative

WMO World Meteorological Organization

LIST OF ACRONYMS

iv
INTRODUCTION AND 

BACKGROUND: FRAMING 

THE INDICATORS

Agriculture is a multibillion-dollar industry in the 

United States; agricultural commodities are among 

the most important of the Nation’s exports and a 

signifcant source of support for rural economies 

across the country. U.S. farm output amounted to 

$132.8 billion as of 2017, contributing to a total of 

$1.053 trillion of national Gross Domestic Product 

and 11 percent of employment when derivative 

industries (e.g., food services, textile production) 

are considered (USDA ERS 2019c). Yet, because 

many types of agricultural production are practiced 

in myriad ways across the United States, specifc and 

local information is foundational for supporting 

efective decision-making by farmers, livestock 

producers, and other land managers. 

Agricultural production is highly sensitive to 

weather and climate (Gowda et al. 2018; Walthall 

et al. 2013; USGCRP 2017). Temperatures, 

precipitation, timing of frost, growing season 

length, soil moisture, pest pressures, and numerous 

other climate and climate-related variables 

infuence agricultural productivity and agricultural 

management decisions. An example of a near-term 

decision that requires reliable climate information 

might be when to plant or harvest; a mid-term 

decision might be what variety of seed to plant 

for the following growing season; and a long-term 

decision might involve whether to make capital 

investments, such as irrigation infrastructure, 

installing subsurface drainage tile, or planting trees 

in an agroforestry system (Hollinger 2009; Prokopy 

et al. 2013; Takle et al. 2014; Dosskey, Brandle, and 

Bentrup 2017). 

Global greenhouse gas emissions from human 

activity are the primary cause of changes in climate 

(USGCRP 2017). Tis report focuses on how 

agricultural production is afected by these changes. 

Although greenhouse gases, such as carbon 

dioxide (CO2), are well mixed in the atmosphere 

(Fahey et al. 2017), changes in climate manifest 

diferently in diferent regions of the globe based 

on global circulation patterns, altitude, latitude, 

proximity to water, and other factors. Hence, 

local production agriculture, which is closely tied 

to local environmental conditions, is afected 

diferently from place to place. At the same time, 

global commodity markets afect the production 

choices and management practices of individual 

producers, as do the habits and preferences of 

consumers, ecosystem processes and constraints, 

and information and technology availability. Te 

interaction between climate, agriculture, and these 

other factors, at the full range of geographical 

scales of activity—from the feld to the globe and 

back again—makes the identifcation of relevant 

indicators highly dependent upon circumstances. 

Climate is important to agricultural production 

and may exacerbate many existing stresses, though 

it acts within the complex economic, cultural, and 

social environment (Walthall et al. 2012) of the 

broader food system. 

Few production decisions are based on weather or 

climate alone. Market prices within a changeable 

global context, transportation costs and options, 

the policy environment, and shifing consumer 

demand can infuence operational and production 

decisions. However, the efects of climate change 

on the larger food system, from farm to fork, are 

important to understanding the economic vitality 

and overall sustainability of production agriculture 

in the United States and for supporting operational 

decision-making in this sector (Brown et al. 2015). 

CHAPTER 1 Climate Indicators for Agriculture

1
Indicators are measurements or calculations that 

tell us the status of a system of interest. Here, 

they provide information about the magnitude, 

timescale, and efects of environmental changes 

relative to historical information.

Indicators may come from direct measurements—

temperatures, for example. Tey may be modeled 

(as in the case of future crop region migration). 

Or they may be mathematically derived based 

on observations or model results—for example, 

total factor productivity (TFP), which provides 

information regarding the changes in agricultural 

productivity resulting from changes in various 

inputs and associated costs, allowing us to 

understand the efcacy of management practices 

and economic investments for production 

outcomes. 

Tis report presents 20 indicators of climate change, 

carefully selected to provide useful and relevant 

information across a range of important agricultural 

production systems in the United States. Together, 

they represent an overall view of how climate 

change is infuencing U.S. agriculture. Individually, 

they may provide useful information for supporting 

specifc management decisions. 

Figure 1-1 describes the categories and relationships 

of the indicators discussed in this report and 

provides a visual layout of the report’s contents. 

Indicators for Tracking Climate Change Effects on U.S. Agriculture

Changing Regional and Local Conditions

AGRICULTURAL CHANGE

Crop and Livestock Indicators

• Animal Heat Stress

• Crop-Growing Region

 Migration

• Leaf Wetness Duration

CHANGING WEATHER

Physical Indicators

• Extreme Precipitation

• Heat Waves

• Nighttime Air Temperature

• Humidity

CHANGING PESTS & DISEASE

Biological Indicators

• Weed Range & Infestation

Intensity

• Insect Infestation in Crops

• Crop Pathogens

• Pesticide Use

CHANGING NATURAL RESOURCES

Physical Indicator

• Soil Moisture

Socioeconomic Indicators

• Crop Insurance Payments

• Total Factor Productivity

• Heat-Related Mortality

 of Agricultural Workers

Phenological Indicators

• Timing of Budbreak in

 Fruit Trees

• Winter Chill Units

• Insect Generations

 Per Season

• Disease Vectors in Livestock

• Pollinators & Pollinator

 Management

CLIMATE

CHANGE

Chapter 1 Climate Indicators for Agriculture

2

Figure 1-1. Climate infuences on agricultural production and food system indicators. 

Changing climate and weather conditions afect biological stressors, such as insects, disease, and weeds, that infuence agriculture. Tey also afect the 

availability and quality of natural resources upon which agriculture depends, such as soil and water. Te indicators included in this report provide an 

overview of the infuence of climate change on agricultural production and the response of diferent components of the food system to such infuence.

Tis report has been written as input to the sustained National Climate Assessment process to provide a 

discrete set of variables that describe linkages between climate trends and variability and U.S. agriculture 

in recent decades. An additional objective is that the indicators themselves (along with the frameworks for 

constructing location- and operation-specifc indicators) provide an information resource that can help 

information service programs, such as USDA’s Climate Hubs, evaluate operational risks posed by climate 

change in specifc production systems across the country. 
1.1 Report Scope, Goals, 

and Considerations

Tis report presents climate change indicators for 

terrestrial production agriculture across the United 

States. Its scope includes commodity and specialty 

crops, as well as animal agriculture. Indicators have 

been selected to represent a breadth of production 

systems across the country—they are not 

intended to provide a comprehensive description 

of the infuence of climate change on any single 

production system. Te intention is rather to 

provide a broad overview of changes relevant to 

a range of production types and to illustrate how 

indicators may be useful for decision-making in 

diferent production systems. Indicators have also 

been selected from each of the major geographical 

areas of the country; this includes regions within 

the continental United States, as well as tropical 

agriculture practiced on island States and territories. 

Agricultural production is embedded in a larger 

food system that encompasses the storage, 

transportation, packaging, distribution, use, 

and disposal of food and food waste. Some of 

the indicators described in the following pages 

are relevant to these broader food system issues, 

and some address the socioeconomic aspects of 

agricultural production; however, developing a 

comprehensive set of indicators of climate efects on 

the food system as a whole was beyond the scope of 

this report. Mitigation of greenhouse gas emissions 

from the agricultural sector was also outside 

the scope of this report, as was consideration of 

aquaculture and forestry. 

Within that context, the goals of this report are 

threefold. Te frst is to document a set of variables 

that show how climate is infuencing agricultural 

production over time. Te second is to enable 

improved management choices that incorporate 

climate information. Te third is to identify highpriority data and research that can enhance the 

capabilities of the agricultural and food system 

communities to accomplish their goals, such 

as increasing production, reducing costs, and 

improving efciency. Tis report serves as a central 

informational source toward helping to accomplish 

each of these goals.

1.2 Report Development 

Five criteria were taken into consideration in 

selecting the indicators in this report:

1. Te indicator must have a clear relationship 

to climate trends. It is not necessary for the 

indicator to respond exclusively to climate, 

however. Even a straightforward measurement 

like temperature is infuenced by multiple 

factors (e.g., urban heat island, land use 

(USGCRP 2017). Terefore, the presence 

of non-climate infuences does not preclude 

consideration of an indicator for this report.

2. Te indicator must be relevant and important 

to agricultural production, food systems, and/

or food security. Te indicators considered by 

this report refect conditions that can infuence 

operations, management, and proftability. An 

indicator may be included whether a change 

in climate condition may be benefcial or 

detrimental for production, management, or 

outcomes. An indicator need only infuence 

those issues to have been considered for this 

report.

3. Te indicator must be based on observations 

and data that provide sufcient historical 

context to understand changes over time and 

what they mean to agriculture. 

4. Te data that support the indicator must 

be available to the scientifc community, 

well documented, and, where appropriate, 

reproducible. 

5. Te overall set of indicators should provide a 

broad view of the interactions of climate change 

and agriculture and address multiple aspects 

of agricultural production, including physical, 

crop/livestock, phenological, biological, and 

socioeconomic characteristics. 

Tis report identifes and provides examples for 

some indicators that meet the criteria listed above; 

it also briefy lists other examples that appear likely 

to provide decision-relevant information going 

forward should additional supporting information 

become available. While no dedicated monitoring 

Chapter 1 Climate Indicators for Agriculture

3
network exists specifcally for the purpose of 

tracking the infuence of a changing climate on 

agriculture or on any other sector, the indicators 

illustrated in this report represent high-priority 

data streams that provide foundational information 

for assessing the changing agricultural landscape 

and for choosing adaptive and technological fxes 

to challenging conditions. Continuity in these data 

sets is important for understanding the types and 

pace of changes in agricultural production and food 

systems now and into the future. Te indicators 

elucidated in this report are not comprehensive, 

however. Other relevant variables that were 

considered by the author team but ultimately 

excluded from this report (for example, because 

the historical observation record was not of 

sufcient length to adequately characterize 

historical changes) are discussed in the introductory 

section of each chapter and may be candidates for 

future consideration. 

Some indicators better represent the above 

criteria than others, afrming that prior research 

investments have ongoing societal benefts. Tis is 

illustrated in Figure 1-2, which shows that for some 

indicators ample and consistently monitored data 

are readily available both currently and over a long 

historical time period, whereas other, perhaps more 

relevant, indicators are based on shorter, less widely 

available data records. For example, the relative ease 

of and obvious need for precipitation monitoring 

across many applications has incentivized its 

continuation and geographical expansion, more 

so than for soil moisture measuring, although 

soil moisture more directly infuences many 

agricultural production outcomes. Each indicator’s 

data record represents a discrete programmatic 

efort; unevenness across the indicators is therefore 

inevitable. Tis report’s authors’ best technical 

eforts have been applied to the selection and 

presentation of the indicators in this report. Te 

quality of the information available from the 

monitoring record dictates the level of uncertainty 

that decision makers will encounter for specifc 

applications of each indicator. 

Tis report contains both metric and imperial units. 

Tis refects the units as they were reported by the 

original data sources to preserve the convention 

used by each particular indicator application. 

Tis was intentional, so that users would be able 

to use the information directly from the data 

source without needing to convert from one 

measurement system to another. Although the data 

and interpretations in this report have undergone 

extensive scientifc scrutiny and review, the report’s 

intended end use is as an illustrative management 

tool for decision makers. 

Numerous types of indicators exist, each with 

diferent advantages and disadvantages. Time series 

of a single variable (e.g., humidity) or a specifc 

aspect of a variable (e.g., extreme precipitation 

occurrence) are the most frequently represented 

types of indicators presented in this report. Figure 

1-2 illustrates that these indicators tend to be most 

directly linked to climate change, especially when 

the variable is also a meteorological indicator.

Indexed indicators mathematically condense 

and distill multiple factors into a single, easily 

tracked numerical variable (such as TFP) for ease 

of use, though the driving cause of change in the 

fnal indicator can be obscured for short-term 

management purposes. Another type of indicator 

aggregates conditions over a given time period 

(such as a 5-day heat wave) or until a potential 

efect occurs (e.g., chilling hours or leaf wetness 

duration) to provide seasonal information for longterm planning and, in some cases (e.g., animal heat 

stress), as forecasts for short-term management 

decisions. Other indicators, such as crop region 

migration, rely on a large number of independent 

data sets, derived variables, and modeled 

information to reveal agriculturally important 

trends. Figure 1-2 shows that such indicators’ 

linkage to climate change is ofen indirect, but 

their inclusion assures that a broad view of the 

interactions between climate change and agriculture 

is conveyed.

4

Chapter 1 Climate Indicators for Agriculture
PHYSICAL INDICATORS

BIOLOGICAL INDICATORS

EP Extreme Precipitation

Soil Moisture

Nighttime Air Temperature

Heat Waves

Humidity

SM

NAT

HW

H

WRI Weed Range and Infestation

Intensity

IIC

CP

PU

AHS Animal Heat Stress

CGRM Crop-Growing Region Migration

LWD

Insect Infestation in Crops

Crop Pathogens

Pesticide Use

Leaf Wetness Duration

CROP AND LIVESTOCK

INDICATORS

PHENOLOGICAL

INDICATORS

SOCIOECONOMIC

INDICATORS

Timing of Budbreak in Fruit

Trees

Winter Chill Units

Insect Generations per Season

Disease Vectors in Livestock

Pollinators and Pollinator

Management

BFT

WCU

IGS

DVL

CIP

HRM

TFP

PPM

Crop Insurance Payments

Heat-Related Mortality of

Agricultural Workers

Total Factor Productivity

Linkage to Climate Change

Data Availability

ADEQUATE HIGHER

INDIRECT DIRECT

PPM TFP SM

LWD

WCUIGS

BFT

H

CGRM

AHS

CIP

EP

HW

NAT

1.3 Report Organization

and Use

Te following chapters of this report focus on 

diferent types of indicators, each of which supports 

production and food system decisions at a range 

of scales.

Chapter 2 describes physical indicators. Tese 

are climate variables, such as temperature and 

precipitation, that have direct efects on agricultural 

production and food systems. Tey are based 

on observed and, in some cases, reanalyzed 

measurements. 

Chapter 3 describes crop and livestock indicators. 

Tese are observations of climate-induced changes 

in agricultural systems, such as heat stress in 

livestock or crop-growing region migration. 

Chapter 4 describes biological indicators, such 

as range and infestation intensity of weeds, pests, 

and disease that afect crop production and animal 

agriculture and are infuenced by temperature, 

precipitation, atmospheric CO2 (carbon dioxide) 

concentrations, and other climate variables. 

Chapter 5 describes phenological indicators, which 

provide information about the timing of seasonal 

activities that are afected by climate conditions—

the date of fowering and pollination for a given 

crop, for example. 

Chapter 6 describes socioeconomic indicators that 

provide information about the relationship of 

climate change to human and economic factors 

in agriculture. 

Good decision-making at all levels depends 

upon many factors, including the strength of the 

monitoring record, the scientifc interpretation 

of the available data, and the accessibility of the 

appropriate information to each decision maker. 

By necessity, agricultural decisions occur on a 

daily basis across a wide range of quickly changing 

biophysical and socioeconomic conditions. It is 

not possible to list every indicator of interest for 

each type of production system in the United States 

because of the localization and specialization in 

agriculture as it is practiced across the country. 

5

Chapter 1 Climate Indicators for Agriculture

Tis report provides examples of each type of 

indicator, where available and reportable according 

to the criteria above, to support decision-making 

at multiple levels. Te current efort builds on the 

recent overview of climate indicators for agriculture 

(Hatfeld et al. 2018) by categorizing additional 

indicator types, adding illustrative examples, and 

providing data sources. Individual operations or 

regions can experience localized conditions that 

may difer from larger trends. As such, this report 

provides a framework for adapting indicators to 

other locations, production types, and food system 

participants’ objectives. 

Figure 1-2 . Data availability and the strength of infuence of climate 

change on each indicator. 

Agricultural and food system decisions occur within a quickly changing 

biophysical and socioeconomic environment; data continuity is therefore 

important for understanding the types and pace of change due to 

changes in climate and for adapting management practices to the desired 

outcomes. Indicators most directly linked to climate are found toward the 

top of the fgure; indicators whose relationship with climate is complicated 

by additional factors are closer to the bottom. Indicators for which the 

most ample and robust data records are available are found toward the 

right of the fgure; indicators for which the data records are less ample 

or robust, yet still consistent with the stated criteria for inclusion, are 

found toward the lef of the fgure. Indicator placement along each axis 

was determined by close examination of the data record followed by 

comparative elicitation among the authors.
Te indicators presented in this report are therefore 

largely illustrative. Tey include examples from 

commodity crops, specialty crops, and perennial 

and annual crops, as well as diferent types of 

livestock production. Examples of socioeconomic 

indicators that are infuenced by climate are also 

included. Some indicators in this report represent 

specifc locations, while others are geographically 

disaggregated to provide a macroscale overview 

for decision makers at that level. Difering types 

of indicators are presented (physical, biological, 

socioeconomic, etc.) to address the range of 

considerations facing production and food 

system operations. 

Each indicator in this report will be of immediate 

use to some decision makers. Although the 

examples selected for each indicator of this report 

ofen represent larger trends, because of the range of 

production types and geographical considerations 

that exist for agricultural production, they are just 

that – examples. Tey do not purport to represent 

the specifc conditions in all production types 

across all locations, which vary widely. For many 

users of this report, therefore, these indicators 

will need to be adapted to accommodate specifc 

locations, production types, varietals/breeds, 

priorities, limitations, and other infuences on 

production agriculture and food system operations. 

For this reason, each chapter provides data source 

information, so that decision makers can search 

for information specifc to their locality and 

goals. Te authors intend for this report to be a 

launching point from which decision makers can 

begin to construct a set of indicators specifc to 

their circumstances that will support real-time and 

longerterm decisions aimed toward meeting their 

specifc and particular objectives. 

Because a relationship to climate is a criterion for 

the inclusion of an indicator in this report, this 

report has no fndings or conclusions chapter. For 

additional information regarding specifc climate 

efects and adaptive options for agriculture and food 

systems, the authors refer the reader to USGCRP 

2018, Brown et al. 2015, and Walthall et al. 2012. 

1.4 Acknowledgments 

Tis report has been jointly drafed by subjectmatter experts from the Federal service and from 

academic and civil society institutions. USDA is 

grateful to the report’s authors and their institutions 

for their time and expertise in service of this efort. 

Te authors wish to thank Tom Driscoll of the 

National Farmers Union, Jimmy Daukas of the 

American Farmland Trust, Rick Mueller of USDA’s 

National Agricultural Statistics Service, Mike Kolian 

of the U.S. Environmental Proection Agency (U.S . 

EPA) and the USGCRP Indicators Working Group, 

Deke Arndt (NOAA NCEI and the USGCRP 

Indicators Working Group, and Mark Shafer of 

the Southern Plains Climate Impacts Planning 

Program Regional Integrated Science Assessments 

for providing initial input regarding what indicators 

would be most useful to the user community. 

Te authors also wish to thank David Brown (USDA 

Agricultural Research Service (ARS), Jerry Hatfeld 

(USDA ARS), Otto Doering (Purdue University), 

Mike Kolian (U.S. EPA), Mark Shafer (University 

of Oklahoma), Laura Edwards (South Dakota State), 

Pat Guinan (University of Missouri), Ken Kunkel 

(National Auronautics and Space Adminnistration), 

Alejandro Plastina (Iowa State University), Jim 

Bunce (USDA ARS, retired), Jef Baker (USDA 

ARS), Mark Schwartz (University of Wisconsin – 

Milwaukee), Julie Wolf (USDA ARS), Jennifer If

(Cornell University), Marc Schenker (University 

of California – Davis), Tom Worth (USDA Risk 

Management Agency), Keith Fuglie (USDA ERS), 

Elizabeth Marshall (USDA NRCS), and Sun Ling 

Wang (USDA ERS) for their substantive expert 

reviews of this document.

USDA and the authors also wish to thank Kristin 

Pintauro at Colorado State University for her 

assistance with the graphics, formatting, and layout, 

Linda Gorman at University Corporation for 

Atmospheric Research for copy editing this report, 

and Marci Baranski for her careful review editing. 

Finally, we are grateful to Karen Slater at UCAR, 

whose coordination and organization were critical 

to this efort.

6

Chapter 1 Climate Indicators for Agriculture
PHYSICAL INDICATORS

Both day-to-day weather and climate—the 

composite of weather conditions over time—are 

important infuences on agricultural practices and 

productivity (Walthall et al. 2012). Averages and 

swings in physical variables, such as temperature 

and precipitation, can infuence many aspects 

of agricultural management and production, 

including crops, animals, the agricultural workforce, 

management practices, and the quality and 

availability of natural resources such as soil and 

water. Te physical indicators described in this 

section are measurements of climate changes and 

changes in natural resources caused by climate 

change that are afecting agriculture.

To be a useful indicator, the variable must have 

been measured accurately enough and for long 

enough to provide information about long-term 

trends and change over time. It should be relevant 

across multiple regions and production systems 

(both crop and animal). Te infuence of the 

variable on agriculture must be well understood and 

documented, with a causal relationship that is clear 

and unambiguous. Tese considerations led to the 

following set of physical indicators being selected 

for this report:

• Extreme precipitation

• Soil moisture

• Nighttime air temperature

• Heat waves

• Humidity

Te fve physical indicators discussed in this 

chapter document trends in key, measurable, 

agriculturally important data across the United 

States. Te strength of these physical indicators 

lies in their robustness, comprehensive measure, 

and length of record. Changes in these indicators 

are closely related to changes in climate. For each 

indicator, there is relatively high data availability 

and high data quality, particularly for the directly 

measurable indicators. With regard to future 

climate-related trends, each of these physical 

variables can be simulated with climate models 

(Kirtman et al. 2013).

Tese fve physical indicators were chosen for 

their documented changes and specifc connection 

to U.S. agricultural production. While there are 

many possible indicators, these have been selected 

as illustrative examples. With regard to water 

availability, the focus is more on natural water 

sources rather than irrigation resources. Broader 

integrative measurements of climate change, such as 

globally averaged surface temperature, atmospheric 

CO2 concentration, and ocean heat content, are well 

documented elsewhere.

Tis set of fve physical indicators is not 

comprehensive and, in the case of soil moisture, 

not as widely measured, yet. Additional relevant 

physical indicators providing a more detailed 

picture of potential agricultural changes do exist. 

Taken together, long-term trends and variabilities in 

these fve indicators provide insight into the efects 

of changes in climate on agricultural productivity, 

food systems, and food security. 

7

CHAPTER 2 Climate Indicators for Agriculture
2.1 Extreme Precipitation

Precipitation is the main provider of water for 

agriculture throughout the country. Hence, 

understanding recent trends and projected 

changes in the amount, timing, and seasonality 

of precipitation is crucial to the sustainability and 

productivity of U.S. agriculture (Karl, Melillo, 

and Peterson 2009). Understanding trends and 

projected changes in extreme conditions, such 

as droughts and intense downpours that lead 

to fooding and increased soil erosion, is also 

critical because such conditions present signifcant 

management challenges for agricultural producers. 

Changes in overall amounts and timing of 

precipitation can greatly infuence crop productivity 

and even the ability of crops to grow in a region 

(Rosenzweig et al. 2002).

Importance to U.S. Agriculture

Expected precipitation conditions are an important 

infuence on rainfed agriculture. Field work, 

planting, crop water use, and many other issues 

are managed around expected temperature and 

precipitation conditions during a growing season, 

and long-term trends in conditions outside of 

historical ranges pose signifcant management 

challenges. Irrigation is a valuable tool for managing 

crop water needs but is also infuenced by recent 

changes in the amount of precipitation overall and 

the amount falling as rain or snow, which afects 

runof, streamfow, and availability of water. 

Data clearly indicate recent increases in the 

size and intensity of rainfall events throughout 

much of the United States (Groisman, Knight, 

and Karl 2012). Despite the adage “rain makes 

grain,” in many places the increase in precipitation 

has been detrimental, as soil and nutrient loss, 

disease occurrence, reduction in feld work days, 

impairment of root growth and function, and 

overall feld wetness have increased due to changes 

in rainfall amounts and timing. Larger events 

during the time when soils are not covered by a 

crop can lead to additional soil loss, even when 

the soil is frozen. Te shif to more precipitation 

outside the main period of the growing season in 

some U.S. regions has created numerous issues for 

managing water in and around felds, caused more 

soil and nutrient loss, and increased the use of 

adaptive methods, such as tiling for drainage water 

management throughout the Midwest (Singh et al. 

2009). Te shif of precipitation outside the growing 

season or away from the time a crop most readily 

needs it (i.e., reproductive or grain-fll periods) 

has also introduced production issues. Limited 

precipitation (drought) is also a serious issue for 

crop loss, especially in some years (2012, 1988) in 

rainfed areas. Where supplemental irrigation is 

used, longer term water supply issues, such as in 

the Colorado River Basin, are impacting overall 

production. 

8

Chapter 2 Climate Indicators for Agriculture
Extreme Precipitation Events in the U.S.

– An Example

Te number of days with heavy precipitation are 

increasing in many places of the country, with 

a statistically signifcant sizable average upward 

trend in the frequency and intensity of extreme 

precipitation events nationally (Figure 2-1) (Kunkel, 

Karl, Brooks et al. 2013a; Kunkel 2003). Te efects 

of this increase in extreme rainfall vary across 

the country and by time of year. Changes in the 

amounts of daily rainfall can have diferent impacts, 

depending upon the season and surface conditions 

(e.g., slopes, soil types, detritus, root mass). Severalinch events may cause minimal damage in the 

middle of the growing season, whereas events of 

less than an inch on frozen ground can create issues 

with soil loss and ponding. 

Data Sources and Limitations

Precipitation measurement has followed a 

standardized procedure for over 100 years 

with the National Weather Service Cooperative 

Observer Program (NWS COOP) network 

(Menne et al. 2012b). Measurements of daily 

total precipitation (including snow) collected at 

locations throughout the country enable longterm comparison of daily and longer accumulated 

totals. Measurements of shorter time period 

accumulations have become much more common 

over the past 20–30 years with the advent of 

automated monitoring, including that conducted 

by the National Weather Service’s NEXRAD (Next 

Generation Weather Radar) system. 

Data from expanding networks of public and 

private sources (e.g., Iowa Environmental Mesonet, 

n.d.) have become more common, although record 

length, metadata availability, and instrument 

standardization may limit their use for long-term 

studies (National Research Council 2009). Te 

spatial density of daily precipitation monitoring 

has also been increased throughout the Nation 

with the development of a citizen science network 

called Community Collaborative Rain Hail and 

Snow Network (CoCoRaHS; Reges et al. 2016). Te 

increase in observations has enabled higher spatial 

resolution measurement of rain events in some 

areas and more efective tracking of the frequency 

of larger events, but with limitations in the length 

of records. 

In addition, the structure of the National Oceanic 

and Atmospheric Administration (NOAA) 

temperature and precipitation network poses 

some reasons for concern. Despite several attempts 

to modernize and improve its capabilities, the 

NOAA Cooperative Observer Network (COOP) 

is still maintained as a volunteer network across 

the country and is declining in numbers (Fiebrich 

2009). Tis has led to difculties in maintaining 

long-term stations, especially in rural areas, which 

presents a challenge to the preservation of the 

ofcial network. 

Figure 2-1. Increasing number of very heavy precipitation days in the 

central United States. 

Red dots indicate the number of daily precipitation events in the upper 

0.3% annually from 1893 to 2010 in the central United States (blue region 

of inset map). Hydrological years start October 1 and end September 

30. Te statistically signifcant linear trends of 2.6% (10 years)−1 for 

the 1893–2010 period (blue dashed line) and 7.4% (10 years) 1 for the 

1948–2010 period (black dotted line, computed from the red dots with 

black outlines) indicate that the trend toward more days of very heavy 

precipitation is positive and accelerating (Groisman, Knight, and Karl 

2012). Graphic courtesy of the American Meteorological Society and 

NOAA.

1893-2010 very heavy daily precipitation days

1948-2010 linear trend of +0.74%/yr

1948-2010 very heavy precipitation events

1893-2010 linear trend of +0.26%/yr

.....

Hydrological Years

Days

1890

0

0.2

0.4

0.6

0.8

1910 1930 1950 1970 1990 2010

1

9

Chapter 2 Climate Indicators for Agriculture
2.2 Soil Moisture

Soil moisture drives many aspects of agricultural 

production and is an integrator of many climatic 

impacts on crop production. Changing atmospheric 

and soil conditions have an impact on soil moisture 

and therefore crop production. Soil moisture is 

defned here as the water contained in the root zone 

of the soil and available for plant utilization (Legates 

et al. 2011). 

Importance to U.S. Agriculture

Soil moisture refects climate and is a key physical 

parameter because it integrates the efects of (and 

interacts with) a variety of meteorological variables, 

namely, precipitation, evapotranspiration, solar 

radiation, temperature, vapor pressure, and wind 

(Legates et al. 2011). It is also infuenced by soil 

properties, such as water capacity, permeability, 

and organic-matter content, as well as by plant 

properties, such as phenology and water use 

(Seneviratne et al. 2010). High-organic-matter soils 

have the ability to maintain plant-available water 

for crop use during extended periods of dryness 

(Hatfeld et al. 2011). Soil moisture infuences 

plant growth and germination of seeds and weeds, 

determines feldwork conditions, and afects soil 

nitrogen status and important organisms within 

soil, such as nematodes, fungi, pathogens, and 

insects (Grifn 1963). Excess soil moisture can ofen 

be related to increased precipitation (discussed in 

a previous section) that can be detrimental to root 

growth and crop development (Rosenzweig et al. 

2002). Overall, soil moisture enables evaluation of 

diferent soil management practices on plant stress 

and production in the context of a changing climate. 

Proper soil management can increase the waterholding capacity of soils for plant-available use, 

thereby increasing productivity (Hatfeld, Sauer, 

and Prueger 2001). 

Soil Moisture in Kentucky – An Example

While long-term (decadal or longer) measurements 

of soil moisture are rare, USDA SCAN (Soil Climate 

Analysis Network) sites ofer some insight into 

changing conditions in specifc locations. Te data 

from the Kentucky SCAN site (fgure 2-2) provide 

an example of what would be expected with longterm increases in soil moisture content. Individual 

soil moisture conditions will vary, based on changes 

in timing of precipitation during the year and 

changing evapotranspiration rates and crop usage, 

and will infuence crop development. 

10

Chapter 2 Climate Indicators for Agriculture
Data Sources and Limitations

Numerous methods exist for measuring soil 

moisture during the growing season, including in 

situ networks maintained by USDA (e.g., SCAN), 

which includes approximately 200 sites mostly 

in agricultural regions, as well as various State 

soil moisture observation networks and remotesensing systems operated by NASA, such as the 

Soil Moisture Active Passive (SMAP) observatory 

(Entekhabi et al. 2010).

Long-term records of soil moisture are limited. 

Although a few State networks have recorded 

regional measurements of soil moisture over the 

past 20–30 years, there is no standardization of the 

depths for in situ measurements. Te USDA SCAN 

network records soil moisture at multiple depths 

(see fgure 2-2). State-supported regional networks 

(ofen called mesonets) report soil moisture 

from multiple depths that do not match those in 

SCAN. In Oklahoma, for example, soil moisture is 

measured at 2, 4, 10, and 24 inches below ground. 

Te longest data records from SCAN and State 

networks extend back to the early 1990s. Te U.S. 

Climate Reference Network (CRN) does in situ

measurements nationally (Bell et al. 2013). Products 

from the North American Soil Moisture Database 

(NASMD) (Quiring et al. 2016) are available online.

Another issue is the location of measurements. 

For agricultural systems, in-feld measurements 

would be the most representative. But in-feld 

measurements using in situ monitoring can be 

impacted by feld activities. Ideally, soil moisture 

measurements should be made in an area 

representing regional soil conditions. However, in 

agricultural regions, in-feld measurements that 

would report soil moisture can be impacted by landmanagement activities. Other problems, such as soil 

disturbance when installing equipment, can afect 

the actual measurements. Drought indexes, such 

as the standardized precipitation index (SPI) and 

the standardized precipitation evapotranspiration 

index (SPEI), have been shown to correlate with soil 

moisture defcit and drought (Vicente-Serrano et al. 

2012).

Remotely sensed soil moisture data represent 

conditions across grid cells of various spatial 

resolutions. NASA’s Gravity Recovery and Climate 

Experiment (GRACE) has provided weekly updates 

of root-zone and surface-soil moisture at a 0.25° 

horizontal resolution (approximately 20 kilometers 

in the midlatitudes) since 2003. Another NASA 

platform, SMAP (Soil Moisture Active Passive), 

senses soil moisture in the top 2 inches of soil at 

horizontal resolutions as fne as about 2 miles. 

Other soil moisture products are termed blended. 

Tese combine soil moisture retrievals from several 

diferent satellite sensors. An example is the NOAA 

Soil Moisture Products System (SMOPS).

A National Research Council study (National 

Research Council 2009) called for a national 

network of county-level soil moisture measurements 

across the United States., but this goal has yet to be 

achieved. However, there are several regional and 

national eforts currently underway endeavoring to 

improve soil moisture measurements. 

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

10

20

30

40

50

60

70

Soil Moisture Percent at 2 inches in depth

Soil Moisture Percent at 4 inches in depth

Soil Moisture Percent at 8 inches in depth

Soil Moisture Percent at 20 inches in depth

Soil Moisture Percent at 40 inches in depth

Year

Soil Moisture Percent

Figure 2-2. Soil moisture data for a location in Kentucky from 

the USDA SCAN network. 

Soil moisture (%) at the Princeton #1 (2005) site is shown at 2 

inches (blue), 4 inches (red), 8 inches (black), 20 inches (gray), 

and 40 inches (green) from 1997 to 2018. Broken lines indicate 

discontinuities in the available data over the observation record. 

Deeper soil moisture conditions show an increase over time, 

while nearer surface conditions have increased only slightly. 

Te increase in precipitation here is likely due to increased 

precipitation in the region during the last decade. (Source: 

USDA NRCS 2019). 

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

10

20

Soil Moisture Percent at 2 inches in depth

Soil Moisture Percent at 4 inches in depth

Soil Moisture Percent at 8 inches in depth

Soil Moisture Percent at 20 inches in depth

Soil Moisture Percent at 40 inches in depth

Year

11

Chapter 2 Climate Indicators for Agriculture
Daytime and nighttime temperatures (maximum 

and minimum) have increased in recent decades 

across most regions of the United States (except for 

some fattening of summer highs in the Midwest). 

Overall warming is projected to continue increasing 

throughout the century (USGCRP 2017). Recent 

warming has been more pronounced in minimum 

temperatures, and this pattern is also expected to 

continue (Qu, Wan, and Hao 2014). Since longterm historical climate data and future climate 

model simulations predominately report only 

daily maximum and minimum temperatures, it is 

assumed that daily minimum temperature typically 

refects nighttime conditions. Increases in both 

average temperatures and warm temperature 

extremes are projected to continue, leading to an 

increase in very warm days and nights (USGCRP 

2017). In regions where summer precipitation 

has also increased, the opportunity for increased 

evaporation could contribute to these changes by 

increasing the dew point, which elevates nighttime 

air temperature. High nighttime temperatures 

adversely afect both human and livestock health 

(Hristov et al. 2018), but also have implications 

for grain and vegetable production (Hatfeld 2016; 

Bisbis, Gruda, and Blanke 2018). Such temperatures 

are of particular concern during the pollination, or 

reproductive, stage of plant development (Hatfeld 

and Prueger 2015). 

Importance to U.S. Agriculture

High minimum temperatures that coincide with 

the grain-flling period tend to increase pollen 

sterility (Matsui et al. 1997), decrease the length 

of the grain-flling period, and ultimately reduce 

yield. Tis impact has been reported in rice (Welch 

et al. 2010), soybean (Salem et al. 2007), and 

wheat (Prasad et al. 2008). In corn, high nighttime 

temperatures during reproductive growth reduce 

the number of kernels and kernel weight. Across 

the Corn Belt, high minimum temperatures during 

several recent years have begun to afect yields 

(Hatfeld et al. 2014).

Te efects of high nighttime temperatures also 

extend to vegetable crops. In tomatoes, minimum 

temperatures above 70°F increase pollen sterility 

and reduce fruit set (Sato, Peet, and Tomas 2000). 

In beans, nighttime temperatures above 80°F result 

in lower yields (Porch and Jahn 2001). 

12

Chapter 2 Climate Indicators for Agriculture

2.3 Nighttime Air Temperature
Extremely Warm Nights in the Southeast U.S. – An Example

Tere are several standards that defne an extremely warm night at a threshold of 68°F (20°C). In outreach and 

extension work, 70°F is ofen used as a representative example of an impact-producing extreme temperature, 

per NOAA (2019), though specifc thresholds will vary by crop, variety, livestock type, location, exposure, and 

so on. Tis threshold is similar to the 20°C value used by the World Meteorological Organization (WMO) to 

defne tropical nights (Sillmann and Roeckner 2008). In the southeastern U.S. 70°F also approximates 90th 

percentile of summer minimum temperature. Te WMO’s extreme indicator for warm minimum temperature 

uses the 90th percentile as a threshold (Zhang et al. 2011). Furthermore, 70°F represents a temperature above 

which agricultural impacts have been reported (e.g., Prasad, Bheemanahalli, and Jagadish 2017). Figure 2-3 

shows the average annual number of nights with a minimum temperature greater than 70°F has nearly doubled 

between the 1954–1963 and 2004–2013 periods in many locations in the Southeast. Te change is particularly 

evident in southeastern Georgia and eastern South Carolina and North Carolina. Near Douglas, Georgia, 

where livestock and tobacco production are common, the average number of nights warmer than 70°F 

increased from 38 in 1954–1963 to 67 in 2004–2013. Sampson County, among the largest vegetable-producing 

counties in North Carolina, experienced an increase from 26 to 47 extremely warm nights between these two 

decades. Poultry production is common in Kershaw County, South Carolina, where nighttime temperatures 

above 70°F increased from 25 to 49 nights between 1954–1963 and 2004–2013. Farther south, prior to 1973, 

Plant City, located in central Florida, never experienced a night during which the temperature remained over 

80°F. From 2004 to 2013, four of these extremely warm nights occurred.

Figure 2-3. Average annual number of nights 

with minimum temperatures greater than 

70°F, averaged over three decades across the 

southeastern United States. 

Each decade—(a) 1954–1963, (b) 1984–1993, 

and (c) 2004–2013—shows an increased area 

where the average number of nights with 

minimum temperatures above 70°F occurred; 

Tthese temperatures exceed the thermoneutral 

zone for cattle, sheep, and goats, above which 

heat stress can occur. (Based on data from Livneh 

et al. 2015.)

Data Sources and Limitations

Daily minimum temperature data are widely 

available, making extreme nighttime temperature 

an excellent indicator of changing climate. Station 

records from the U.S. Historical Climate Network 

extend back to the late 1800s in some cases, with 

a large number of station records starting from 

the 1950s. Tere also exist several high-resolution 

gridded data sets that allow the indicator to 

be mapped. Although extreme temperatures 

on individual nights are highlighted in fgure 

2.3, related indicators focused on consecutive 

occurrences of high nighttime temperatures and 

diferent crop- and region-specifc temperature 

thresholds can be considered.

Data from Livneh et al. (2015) cover the period 

from 1950 to 2013 and include daily maximum and 

minimum air temperature at a spatial resolution of 

1/16 degree (approximately 6 km x 6 km). Te data 

grid is based on observations from several thousand 

stations comprising the U.S. NWS COOP network 

(Menne et al. 2012b). Station data are statistically 

interpolated to the Livneh grid and adjusted to 

account for elevation. Tese data are available as 

part of the Applied Climate Information System 

(2019). 

13

Chapter 2 Climate Indicators for Agriculture
2.4 Heat Waves 

Heat waves are defned as periods of consecutive 

days above a given threshold temperature or 

beyond a certain probability level at the high 

end of a temperature distribution (e.g., two or 

more consecutive days with daily maximum 

temperature above 95°F or fve consecutive days 

with daily maximum temperature exceeding the 

90th percentile; Smith, Zaitchik, and Gohlke 

2013). Consecutive high-temperature days above 

species-specifc thresholds interfere with normal 

biophysical functioning of agricultural plants 

and animals. Te United States is projected to 

experience increasing temperatures (USGCRP 

2017), and this, in turn, is expected to lead to more 

frequent, more intense, and longer duration heat 

waves (IPCC 2012; Coumou and Rahmstorf 2012) 

with negative impacts on agricultural productivity. 

Current high-emission-scenario projections of 

heat wave intensity show increases in all regions of 

the United States, with changes in the intensity of 

once-per-decade 5-day events rising 11°F–13°F by 

midcentury (USGCRP 2017).

Importance to U.S. Agriculture

Heat waves during the growing season, sometimes 

associated with periods of drought (Mazdiyasni 

and Aghakouchak 2015), disrupt animal and plant 

productivity in agricultural systems. Additionally, 

periods of high humidity in combination with 

periods of high temperature (Peterson et al. 2013) 

lead to extreme discomfort for agricultural animals 

(Mader, Johnson, and Gaughan 2010) and the 

people who manage them in outdoor environments. 

(See indicators 3.1, 6.2, and 6.3 for discussions of 

animal heat stress, TFP, and heat-related mortality, 

respectively.) Climate adaptation measures may 

reduce biophysical impacts but contribute to 

increased production inputs that consequently 

reduce TFP (see indicator 6.2). 

 

14

Chapter 2 Climate Indicators for Agriculture
Figure 2-4. a) Annual average number of heat wave days (2 or more 

consecutive days with heat index >90oF) for 1989–2018. b) Trends in the 

number of annual heat wave days (1989-2019). Colored areas indicate 

results above 95% signifcance.

Many areas in states in the Southeast, mid-Atlantic, and upper Midwest 

saw increases of 5–10 heat wave days per year in individual years during 

this period (Smith, Zaitchik, and Gohlke 2013, updated for 1989-2019 

with post-Smith et al. 2013 publication data drawn from their source). 

Figures redrawn by NCAR (2019) per Smith et al. 2013 methodology, 

dataset, and process.

50N

40N

30N

50N

40N

30N

120W 100W 80W

120W 100W 80W

B

A

0 1 2 3

0 20 40 60 80 100 120 140

-3 -2 -1

Trend over 1989-2018

Days/Year

Heat waves whose defnition includes the role 

of humidity (as shown in fgure 2.4) generally 

have higher frequency of occurrence in the U.S. 

Southeast, while those defned only by temperature 

have highest frequency of occurrence in the U.S. 

Southwest. 

Data Sources and Limitations

Heat waves may be defned by using daily 

maximum, minimum, or average temperatures, 

or a combination of temperature and humidity, 

depending on application (Robinson 2001). Tese 

data, as well as moisture and wind data, may be 

obtained from the NOAA National Centers for 

Environmental Information (NCEI) database 

(2019). Tis information can also be obtained from 

regional mesonets (National Research Council 

2009). Occurrences and characteristics of heat 

waves are determined from station records, either 

directly or through data assimilation methods 

that combine observations and modeling (Smith, 

Zaitchik, Gohlke 2013). Heat waves experienced 

at individual locations (e.g., felds or feedlots) 

may be hotter than those estimated by averaging 

from nearby measuring stations or grid points of a 

regular mesh (Zhang et al. 2011). 

National Weather Service Heat Index for Heat Waves of 2 or More Consecutive Days 

– An Example

Te NWS heat index is calculated using temperature and relative humidity, with factors such as vapor pressure, 

wind speed, and human activity levels used to parameterize the equation defning the apparent heat wave 

temperature, expressed in degrees Fahrenheit (Smith, Zaitchik, and Gohlke 2013). Te number of 2-ormore-days heat wave events and recent trends thereof over the period 1989–2018 using the NWS heat index 

temperature are shown in fgure 2-4. 

15

Chapter 2 Climate Indicators for Agriculture
2.5 Humidity

Atmospheric humidity is a fundamental 

variable that plays a key role in formation of 

precipitation and dew. It is also important for 

regulating moisture loss from plants, animals, 

and humans through regulation of transpiration 

(plants; DeJonge et al. 2015) and evaporation 

and respiration (animals and humans; Mader, 

Johnson, and Gaughan 2010). Tese moistureloss processes, in turn, infuence the temperature 

and physiological functions, including growth 

and reproduction, of living systems (Farooq et al. 

2009). Models for simulating precipitation, dew 

formation, and thermal stress on plants, animals, 

and people all use atmospheric humidity as a core 

variable. Changes in Earth’s climate dynamics 

lead to changes in humidity at global, regional, 

and local levels (Feng et al. 2016). Atmospheric 

humidity, measured and reported as dew point 

temperature, has increased at most regional 

scales in the United States over the past 50 years 

(Robinson 2000; Brown and DeGaetano 2013). 

It is a key factor in plant (DeJonge et al. 2015) 

and animal (Mader, Johnson, and Gaughan 

2010) stress indexes and provides a measure of 

atmospheric water available for agriculture in 

the form of precipitation or dew (Xu, Yan, and 

Tang 2015). Changes in cropping patterns in 

several places, such as from rangeland to row crop 

(Wright and Wimberly 2013), have also shifed 

evapotranspiration timing and humidity during 

the season. 

Importance to U.S. Agriculture

In dry climates, dew accumulation on plant and 

soil surfaces can provide a signifcant contribution 

to the plant-available water over the diurnal cycle 

(Xu, Yan, and Tang 2015). Afer harvest, managing 

humidity levels in stored produce is an issue for 

both commodity and specialty crops (Hurburgh 

2016). Increased humidity seems to also have been 

a factor in increasing minimum temperatures 

(addressed in the previous section; Randall et 

al. 2007). Timing of humidity can afect crop 

evapotranspiration during limited precipitation 

times, reducing the potential yield loss. Most of 

these issues occur during the reproductive period 

in corn, or the grain fll period in corn/soybean or 

other grains, when stress can lead to larger yield 

losses. Humidity infuences plant disease as well; 

see the leaf wetness duration indicator (section 3.3) 

for additional information.

Dew Point Temperatures Across the United 

States – An Example

Trends in regional and local atmospheric humidity 

can be infuenced by land use and agricultural 

management practices, such as subsurface tile 

drainage or irrigation. If relative humidity is 

constant, then atmospheric moisture, expressed 

as specifc humidity, increases exponentially with 

temperature. Figure 2-5 provides a map of recent 

summer trends in dew point temperatures for the 

United States. 

16

Chapter 2 Climate Indicators for Agriculture
Figure 2-5. U.S. summer dew point temperature trends. Trends (°C/yr) in June–July–August surface dew point temperature are mapped across the U.S. 

between 1947 and 2010. Black circle outlines indicate signifcance at the 5% level, and the size of the circle is proportional to the magnitude of the trend. 

Much of the country is becoming more humid, with many regions signifcantly so (Brown and DeGaetano 2013). 

Magnitude and sign of trend

-0.08oC/yr

-0.04oC/yr

-0.013oC/yr

+0.08oC/yr

+0.04oC/yr

+0.013oC/yr

Data Sources and Limitations

Te NOAA National Centers for Environmental 

Information (NCEI 2019) database has records of 

dew point temperatures for the past 100 years. Te 

North American Regional Reanalysis (Mesinger et 

al. 2006; NCEI 2019) provides three-dimensional 

felds of moisture as well as other data for evaluating 

past trends (Feng et al. 2016). Historical trends in 

dew point temperatures measured at surface stations 

may have been infuenced by land-use changes (e.g., 

irrigation or wetland drainage), instrumentation 

changes in the mid-1990s, and breaks in the record 

(Brown and DeGaetano 2013; Robinson 2000), as 

well as rising surface temperatures. Land-cover 

changes, such as increases in row crop acreage, have 

been associated with lower dew point temperatures 

(Sandstrom, Lauritsen, and Changnon 2004). 

Precipitation changes and dew point changes are 

related. Additional rainfall does add to surface 

dew points. Higher atmospheric moisture content 

(dew points) would support additional rainfall 

(Kunkel, Karl, Easterling, et al. 2013b). Varieties of 

row crops difer in their water use (transpiration) 

due to varietal diferences in water-use efciency. 

Tese varietal diferences are afected by seasonal 

atmospheric demands, crop conditions, soil 

moisture, etc. However, the larger shif from small 

grains (oats, wheats, etc.) to longer-season annuals 

(corn/soybean) has had a larger efect on the 

regional climate than the more subtle diferences, 

in corn (maize) varieties, for example (Alter et al. 

2018, Mueller et al. 2016).

17

Chapter 2 Climate Indicators for Agriculture
Indicators in this category can apply to both 

commodity and specialty crops. Livestock 

includes poultry, meat, milk, and eggs. Crop 

and livestock indicators typically use basic 

meteorological variables, as well as physical and 

biological indicators, to link food, fber, and 

biofuel production factors to climate change. For 

economic reasons, agricultural crops and animals 

are raised preferentially in regions having favorable 

ecological services that promote growth and good 

health (Power 2010). Climate conditions are central 

to these ecological services, and climate change 

can bring stresses to both plants and animals that 

impair growth and reproduction, creating the need 

for costly management interventions to mitigate 

these unfavorable conditions. Conversely, climate 

conditions can change in such a way as to provide 

new opportunities in regions that formerly were 

perceived as having climatic barriers to agricultural 

production; for example, the recent upward 

trend in warm-season precipitation in North 

Dakota (USGCRP 2017) has contributed to a corn 

harvested-acreage increase from an average of 0.64 

million acres for 1995-1999 to 3.06 million acres for 

2015-2019 (NASS 2019b).

Te relevance of some crop and livestock indicators 

may be infuenced by socioeconomic factors, such 

as public policy on trade or crop insurance, that 

themselves may be linked to climate change both 

locally and internationally. Some socioeconomic 

factors contributing to crop and livestock indicators, 

such as transportation and labor costs, are difcult 

to document over a suitable historical period. Some 

management factors (e.g., irrigation availability, 

cost, and policy) ameliorate or amplify the efects 

of climate change in specifc regions. 

Crop and livestock indicators can be specifc to 

individual crops or animals (e.g., moisture stress, 

heat stress) and growing regions, or they may be 

nationwide indicators of the status of agricultural 

response to climate change (e.g., production 

regions, productivity). University and privatesector contributions to development of indicators 

and relevant crop or animal databases (e.g., 

Enviroweather 2019; Oklahoma Mesonet, n.d.) 

have improved opportunities for development of 

real-time indicators to mitigate negative impacts of 

short-term climate fuctuations and extremes. Te 

importance of such indicators (e.g., leaf wetness 

duration for apple scab occurrence on apples and 

heat stress indexes for livestock) for real-time 

management provides incentive to monitor trends 

in such indicators with changing climate.

Te usefulness of crop and livestock indicators is 

enhanced by long records of data highly relevant to 

climate change and agriculture. Te USDA National 

Agricultural Statistics Service database (USDA 

CROP AND LIVESTOCK 

INDICATORS

CHAPTER 3 Climate Indicators for Agriculture

18
NASS 2019a) provides a rich archive of factors that 

are themselves crop and livestock indicators (e.g., 

crop yield, production regions) or contributors to 

more aggregated indicators (e.g., TFP). Analogously, 

the NOAA NCEI (2019) database provides a rich 

archive of weather observations—extending, at 

some locations, more than a century into the past—

for assessing climate trends of relevance 

to agriculture. 

Livestock (including poultry) are managed in 

both open range and confned environments, 

the latter providing opportunities for various 

levels of protection from extreme conditions. 

Exposure to heat, cold, high humidity, and extreme 

precipitation (blizzards, mud) are the primary 

conditions contributing to lower performance. 

Some combinations, such as heat and high humidity 

or precipitation followed by extreme cold, can lead 

to stress or death. Since performance (e.g., gaining 

weight, producing milk, laying eggs, breeding 

successfully) correlates to animal comfort level, 

many indicators are used operationally to estimate 

or predict departures from comfortable climate, and 

hence, reduction in performance.

Consideration of biophysical needs and limits 

of plants, as well as disease avoidance, suggests 

numerous potential climate change conditions 

afecting crop production: heat stress, water stress, 

freezing conditions, dormancy requirements, 

surface wetness, and so on. Quality factors (e.g., 

nutritional value, marketability) also may be 

impacted by climate change, and thereby may 

be candidates for climate change indicators. Te 

purpose of this report is to provide illustrative 

examples rather than a comprehensive set of 

indicators. Candidate indicators in this category 

could be site specifc (and thereby could be used 

for short-term management as well as climate 

indicators), and some are most applicable at 

regional scales, with potential applications to 

agricultural infrastructure and policy. 

Te crop and livestock indicators chosen for 

this report depict three diferent components of 

agricultural systems that are vulnerable to climate 

change: 

• Animal heat stress

• Crop-growing region migration 

• Leaf wetness duration 

Tese three indicators are examples, respectively, 

of how animal performance, areal crop yield, and a 

leaf microclimate condition favorable to pathogens 

can signal agricultural impact of climate change. 

Many other animal or crop-specifc indicators 

could be developed if future climate changes or 

new prospective production regions call for a more 

comprehensive list. 

Climate Indicators for Agriculture

19

CHAPTER 3, cont.
3.1 Animal Heat Stress

Heat stress may lead to declines in performance 

(e.g., daily weight gain, egg production, feed 

conversion rate) or mortality. Stress indicators, 

which are based on temperature, humidity, wind 

speed, and exposure to short- and long-wave 

radiation (Mader, Johnson, and Gaughan 2010), 

may change as climate change alters one or more 

of these factors. Animal heat stress indexes provide 

metrics for monitoring the animal comfort zone 

in a changing or more variable climate (Oklahoma 

Mesonet 2018). Heat stress indicators have been 

developed based on conditions outside the range 

of those that promote good health and avoid stress 

and discomfort in animals raised for production 

of meat, milk, and eggs (St-Pierre, Cobanov, and 

Schnitkey 2003; Nayak et al. 2015). 

Importance to U.S. Agriculture

Locations providing environmental conditions that 

allow access to feed, water, and essential nutrients at 

low cost are favored for animal production. Specifc 

types of animal agriculture have been historically 

widely practiced in regions where natural (outdoor) 

climate conditions are favorable for such animals 

to thrive. Some animal production systems, such as 

swine and poultry, primarily use confned livestock 

buildings equipped with mechanical ventilation 

systems (Vitt et al. 2017; St-Pierre, Cobanov, and 

Schnitkey 2003). For such facilities, air handling 

systems are necessary to optimize indoor climate 

conditions. As temperatures rise according to 

projected heat wave conditions for many regions 

of the United States (USGCRP 2017), increasing 

the ventilation rates will be insufcient to manage 

heat stress in confnement buildings. Air treatment 

systems, such as earth–air heat exchangers or direct 

evaporative cooling units, may become necessary, 

which will increase costs of production (Vitt et al. 

2017; Key, Sneeringer, and Marquardt 2014). 

Heat Stress in Dairy Cattle – An Example 

Te most widely studied climate indicators for 

animal agriculture are associated with cattle 

meat and milk production (Silanikove 2000; Key, 

Sneeringer, and Marquardt 2014; Chebel et al. 

2004). Dairy production, in particular, is carried 

out widely in almost every State in the continental 

United States. Internal heat generation by normal 

metabolism is exacerbated in lactating dairy cows 

under high milk production (Polsky and von 

Keyserlingk 2017). Te ability of these animals to 

shed heat becomes increasingly compromised in 

an environment of rising ambient temperature and 

humidity. An example of an upward trend in dairy 

cattle heat stress in northeast Colorado, a region 

characterized by its high concentration of dairy 

production, is given in fgure 3-1. Milk production 

declined by 0.2 kg/unit THI (temperature-humidity 

index) for THI greater than 72 (Ravagnolo and 

Misztal 2000). Te THI (in degrees Fahrenheit) 

is calculated as follows (Hahn and Mader 1997; 

Gaughan et al. 2008): 

Chapter 3 Climate Indicators for Agriculture

20

THI = (0.8 * ambient temperature) + {[(relative 

humidity/100) * (ambient temperature − 14.4)] + 46.4} 
Figure 3-1. Trend in the July maximum one-day temperature-humidity index (THI) from 1980 to 2018 derived from NOAA ASOS data for station KSTK at 

Sterling, Colorado. 

Te THI is calculated from the daily maximum temperature and minimum relative humidity (Ravagnolo and Misztal 2000). Te increasing trend at this 

location has not reached levels of increased mortality (the “severe stress” range), but reductions in milk production have occurred within the “mild stress” 

range of 0.2kg/THI unit over the observation period. 

Data Sources and Limitations

Heat stress, or THI, is easily calculated from data 

regularly collected across the United States and 

can be included as a part of standard weather 

forecasts and future climate projections in regions 

of rangeland cattle production. Te primary 

uses of stress indexes for animals are currently 

for research, as extreme heat events continue to 

increase in frequency (USGCRP 2017). Private1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

70

72

74

76

78

80

82

THI

No Stress Stress increases with THI Severe Stress

Temperature-Humidity Index Trend from 1980-2018

sector consultants and agriculture-extension 

researchers are providing forecasts for certain 

animal agricultural sectors. Temperatures and 

humidity levels provided by local weather forecasts 

can be used to forecast cattle comfort and inform 

producers of the need for adaptive actions. An 

example of such a service is information that is 

currently being provided by the Oklahoma Mesonet 

Cattle Comfort Advisor (Oklahoma Mesonet 2018).

Chapter 3 Climate Indicators for Agriculture

21
Changes in crop-growing regions can cause 

substantial societal disruptions to communities 

due to changes in land values and employment 

opportunities (USGCRP 2018, chap. 10). 

Major growing regions for specifc crops can shif

if climate conditions become more (Mueller et al. 

2015) or less favorable (Walthall et al. 2012) for 

crops to achieve their yield potential in historic 

locations (Liang et al. 2017; Leng and Huang 

2017; King et al. 2018). Climatic causes of regional 

shifs may be related to defcient or excess rainfall, 

extreme high temperatures, insufcient cold 

temperatures for required dormancy, changes 

in humidity, and length of the frost-free season 

(Iizumi et al. 2017). But crop-growing regions 

may also change due to value of land or irrigation 

water, availability of labor, market developments, 

technological developments (Tester and Langridge 

2010), or numerous other reasons (Cho and 

McCarl 2017). 

Importance to U.S. Agriculture

Expansion of cropping into regions previously 

considered climatically marginal areas has been 

possible over the 20th century and into the 

21st because of technological changes (Tester 

and Langridge 2010), principally, wider use of 

irrigation (USDA ERS 2019b), improvements 

in machinery, cultivars with higher tolerance to 

extreme climate conditions and pests, improved 

pesticides, and increased use of soil-water drainage 

(e.g., contouring slopes, gridded tile drainage, and 

irrigation systems). Tese measures carry high 

implementation costs that require many years to 

recover. By contrast, previously high-producing 

regions may become candidates for changes in 

cropping due to increased heat or drought or 

reduced availability of irrigation water (King et al. 

2018; Grassini, Yang, and Cassman 2009; Takle et 

al. 2013).

22

Chapter 3 Climate Indicators for Agriculture

3.2 Crop-Growing Region Migration
Figure 3-2 provides maps of contemporary (2018) 

yields and projected changes (2000–2050) in 

yield of rainfed corn for assessing the efects of 

climate change (as opposed to other drivers, such 

as new technologies). Impacts of climate change 

in the major corn-growing regions of the United 

States, fgure 3-2(a), are shown in fgure 3-2(b) 

and range from areas being lost to production 

(red) to new areas potentially being brought into 

production (blue). Areas in yellow are projected 

to experience yield declines, while areas in green 

could see increases from current yield trends by 

2050. Areas indicated as being lost to production 

under rainfed conditions but that have access 

to irrigation water may be able to suppress yield 

declines. However, midcentury temperature 

projections (USGCRP 2017) indicate even 

irrigation may not be sufcient to retain current 

trend yields (Shauberger et al. 2017). 

Data Sources and Limitations

Te primary source of data for historical trends 

that reveal crop migration regions is the USDA 

NASS (2018) database. Te database has data, 

maps, and charts on numerous factors relating to 

crop migration for a wide variety of crops grown 

in the United States. Projected future trends in 

crops grown, yield, production, and so on are 

generally available only from research studies on 

specifc crops (usually commodity crops: corn, 

soybean, wheat, rice, cotton). Limited studies 

are available for specialty crops. Actual crop 

migration will be captured by the USDA NASS 

observational database and should be identifable 

through comparison with projections.

Corn for Grain 2018

Yield Per Harvested Acre by County

for Selected States

U.S. Department of Agriculture, National Agricultural Statistics Service

B us hels P er Ac re

Not Estimated

< 100

100 - 124.9

125 - 149.9

150 - 174.9

175 - 199.9

200 +

Bushel Per Acre

a

b

Baseline area lost

Yield lost >25% of baseline

Yield lost 5% to 25% of baseline

Yield change within 5% of baseline

Yield gain 5% to 25% of baseline

Yield gain >25% of baseline

New area gained

Rainfed Corn Production – An Example 

Historical cropping region migration for a particular crop can be quantifed by mapping changes in annual 

values of yield, acres planted, or acres harvested, as recorded by USDA NASS (2019a) historical records. 

Projecting future migration is more challenging, but the climate component of migration can be estimated 

from projections of yield, productivity, production, length of growing season, temperature variability, and 

thresholds (e.g., chilling hours for fruit production), as well as rainfall mean, variability, and seasonality. Local 

land and landscape characteristics, such as soil quality, slope, suitability for machinery, and access to irrigation 

water, also are factors that limit migration. Biophysical models driven by projections of future temperature and 

precipitation provide a method of tracking the infuence of climate change on crop-growing regions. 

Figure 3-2. Projected geographic distribution of changes in yield for 

rainfed corn by midcentury (a proxy for change in regions where corn 

production would be proftable). 

Map (a) shows maize yield per harvested acre in 2018 (USDA NASS 

2018). Map (b) shows changes in yield for rainfed maize (from 2000 

to 2050) based on climate conditions projected by the Canadian global 

climate model (Takle et al. 2013). Projected regions lost to production 

(red) or becoming favorable for production climatically (blue) would 

drive changes to the overall corn-growing region.

23

Chapter 3 Climate Indicators for Agriculture
Annual precipitation has increased over the U.S. 

northern and southern plains, the Midwest, and the 

Northeast over the 20th century and into the 21st 

(USGCRP 2017), especially during the growing 

season. Longer periods of direct rainfall, as well as 

longer periods of water available from moist soils, 

extend the periods of wet leaves on agricultural 

and horticultural plants. Leaf wetness, as the name 

implies, is free water on vegetation surfaces, both 

upper and lower (Rowlandson et al. 2015). Sources 

of leaf wetness include rain and fog events, dew 

formation, and overhead irrigation. Dew forms 

on a plant surface when its radiating temperature 

drops below the dew point temperature and can 

result from downward transfer of water from the 

atmosphere (dewfall) or upward fux of water 

vapor from soil (dewrise; Jacobs, Heusinkveld, and 

Berkowicz 2002; Guo et al. 2016). 

By contrast, precipitation decreases in the U.S. 

Southwest and Southeast over the 20th century and 

into the 21st (USGCRP 2017) have reduced periods 

of free water on plant surfaces. Local changes in 

rainfall and atmospheric humidity due to climate 

change will increase or decrease dew occurrences 

and durations, and therefore change the plant 

disease potential for many agricultural 

and horticultural crops.

Importance to U.S. Agriculture

Leaf wetness duration (LWD) is an important 

factor for determining the extent of favorable 

conditions for many bacterial, fungal, and oomycete 

(water mold) diseases that fourish in moist 

surface conditions (Sentelhas and Gillespie 2008). 

Furthermore, as shown by Van Hove et al. (1989), 

water flms on leaf surfaces strongly increase 

adsorption of NH3 (ammonia) and SO2 (sulfur 

dioxide) and play a major role in the interaction of 

these gases with the plant. Seasonally accumulated 

LWD exceeding species-specifc thresholds can lead 

to infections and signifcant reduction in produce 

quality. For example, at an average temperature 

of 15°C (59°F), light infection of apple scab will 

result if leaves remain wet for 10 hours and heavy 

infection will occur for a dew duration of 21 

hours. At 25°C (77°F), the respective durations for 

infection to occur are 13 and 28 hours (American 

Phytopathological Society 2018). Apple scab 

(Venturia inaequalis) is a fungus disease that can 

cause economic damage of 70 percent or more to 

fresh fruit if uncontrolled during the spring months 

(Biggs and Hickey 2019).

24

Chapter 3 Climate Indicators for Agriculture

3.3 Leaf Wetness Duration (LWD)
Trends in Leaf Wetness Duration – An Example 

A proxy for dew duration can be constructed from widely measured values of dew point temperature (or 

relative humidity) and air temperature. Global increases in minimum daily temperatures and atmospheric 

moisture (USGCRP 2017) suggest that trends in these variables during the growing season would be precursor 

indicators of possible changes in LWD. Ideally, hourly values of long-term temperature and humidity, 

together with a surface-energy balance model, are needed to evaluate trends in dew duration. However, one 

simple model for exploring dew duration trends uses the assumption that dew forms on vegetation when 

the humidity at 1.8 m exceeds 87 percent (Wichink Kruit, Jacobs, and Holtslag 2008; Van Jaarsveld 2004). 

Application of this simple model applied to daily July values of relative humidity is shown for Sterling, 

Colorado (fgure 3-3). 

Figure 3-3. Tirty-one-day average of the July daily maximum values of relative humidity from 1980 to 2018 derived from NOAA ASOS data for station 

KSTK at Sterling, Colorado. 

Te upward trend demonstrates an increasing probability of apple scab events at this location.

Data Sources and Limitations

LWD can be measured with electronic sensors 

that measure changes in resistance or dielectric 

constant of a specially wired plate upon which 

dew forms. Te sensor reports dew duration 

for the specifc location and orientation of the 

plate, but this measurement may not represent 

other levels within the crop canopy, such as over 

the entire crown of a tree, or other points in the 

feld or orchard. Additional challenges for using 

electronic LWD sensors include type of paint on the 

surface, calibration, maintenance, and feld access 

(Rowlandson et al. 2015). LWD is not measured 

routinely by weather reporting networks, with the 

1975

70

75

80

85

90

95

100

1980 1985 1990 1995 2000 2005 2010 2015 2020

Year

Relative Humidity (%)

Relative humidity Trend from 1980-2018

Dewfall Threshold

exception of specialized agricultural networks 

(e.g., Enviroweather 2019; USDA NRCS 2019). 

An alternative to using sensors for measuring LWD 

is the use of observational or statistical models, or 

a combination thereof (Kim, Taylor, and Gleason 

2004; Sentelhas and Gillespie 2008). Tese models 

can be simple, using readily available measurements 

of air temperature and dew point temperature 

or relative humidity. More complex models may 

incorporate the surface-energy balance (Wichink 

Kruit, Jacobs, and Holtslag 2008; Van Hove et 

al. 1989). Statistical models are limited by being 

applicable only to the area from which the statistical 

data were collected.

25

Chapter 3 Climate Indicators for Agriculture
BIOLOGICAL INDICATORS

Pests can be defned as those organisms (insects, 

plants, microbes, animals) that can reduce the 

quantity and quality of any product obtained from 

managed agricultural systems. Changes in climate 

are projected to afect the establishment, spread, 

and impacts of pest species within agricultural 

systems. For example, the continuous increase in 

atmospheric CO2 concentration directly stimulates 

the growth and fecundity of weedy species, but also 

has implications for host plants, insects, and plant 

pathogens. In addition, increased temperatures, 

particularly warmer winters, may represent a 

removal of thermal constraints that would allow 

northward migration of crop and livestock pests. 

Overall, biotic losses in agricultural systems can be 

substantial, and can exceed 50 percent, depending 

on the crop system (Oerke 2006). Consequently, 

the nature and outcomes of impacts arising from 

environmental change are of immediate concern. 

However, the potential outcomes of rising CO2 and 

environmental perturbations on pest biology are 

difcult to project and quantify, in part because it 

is unclear whether current management paradigms 

(primarily chemical application) and future adaptive 

actions will be sufcient to negate any additional 

pest pressures. Additional information is needed 

to help determine the extent to which changing 

climate and increased atmospheric concentrations 

of CO2 will alter current levels of pest damage in 

agriculture (e.g., Ziska et al. 2018). 

Useful sources of such information are observations 

of pest demographics that are afected by 

climate and subsequent changes in management 

practices, primarily pesticide applications. Tis 

section describes observed changes in the range, 

distribution, and management of some known 

agronomic pests (e.g., kudzu, corn earworm, wheat 

scab) that are, or will be, infuenced by changing 

climate conditions. Te following indicators are 

discussed here:

• Weed range and infestation intensity

• Insect infestation in crops

• Crop pathogens

• Pesticide use

Relevant studies indicate that changing climate, 

specifcally, warming temperatures and rising 

levels of CO2, can afect agronomic and invasive 

weed demography of hard-to-manage weeds, such 

as Palmer amaranth (Amaranthus palmeri), and 

can induce latitudinal or altitudinal shifs in a 

major pest of corn production, corn earworm. In 

addition, research, modeling, and observations over 

the past decade have shown a number of linkages 

between climate conditions and the development 

and severity of wheat scab, a disease that can 

reduce grain weight and quality. Finally, there is 

increasing evidence that warmer temperatures and 

additional CO2 can reduce the efcacy of pesticide 

management, with subsequent environmental and 

economic costs (Ziska 2016). 

Tese three biological indicators (weeds, insects, 

pathogens) and one pest management indicator 

(pesticide use) are examined in the context of 

climate change and rising levels of CO2. Tese 

indicators were chosen based on their relevance to 

U.S. agricultural production and data set availability. 

Tey were chosen as illustrative examples—they do 

not represent a comprehensive listing of possible 

biological indicators of climate infuences on 

agriculture for all possible production systems. 

CHAPTER 4

26

Climate Indicators for Agriculture
Among pests, weeds lead to the greatest direct 

economic losses and the greatest control costs in 

crop production (Pimentel et al. 2000). Many of the 

worst invasive species for warm-season crops in 

the southern United States originated in tropical or 

warm temperature areas; consequently, northward 

expansion of these invasive species may accelerate 

with warming (Patterson 1993). 

Observations show that some weed species, 

including herbicide-resistant weed species, such 

as Palmer amaranth (Amaranthus palmeri), are 

migrating or will migrate northward as the U.S. 

climate warms (Patterson et al. 1999; Kistner and 

Hatfeld 2018). Overall, climate-induced changes 

are likely to afect weed demographics, population 

intensity, and weed management (Ziska and 

McConnell 2016).

Importance to U.S. Agriculture

Kudzu (Pueraria montana var. lobata), an invasive 

vine from eastern Asia that has colonized much of 

the southeastern United States, provides an example 

of how weed infestation can afect U.S. agriculture. 

Kudzu is an alternate host for a number of viruses 

including Asian soybean rust, soybean vein cecrosis 

virus, and tobacco ringspot virus, and has a direct 

impact not only by outcompeting forests and 

taking over agricultural land, but also by spreading 

these pathogens (Blaustein 2001, Zhou et al. 2018, 

Aboughanem-Sabanadzovic et al. 2014). Kudzu 

infests approximately 30,000 km2 and is potentially 

increasing its areal coverage by over 500 km2 per 

year (Forseth and Innis 2004). Kudzu costs the U.S. 

economy over $500 million per year as lost crop 

and forest productivity, expenditures for control, 

27

Chapter 4 Climate Indicators for Agriculture

4.1 Weed Range and Infestation Intensity
and damage to property (Blaustein 2001). Changes 

in its northern migration and spread with warming 

winters have been assessed since 1991 (Coiner 

et al. 2018). It is likely that warming winters will 

also afect the demographics and range of other 

agronomic and invasive weeds, with subsequent 

impact on managed systems in the northern 

United States.

Kudzu – An Example 

Tere is evidence that warmer winters may have 

contributed to a northward shif in the northern 

limit of kudzu (Ziska and McConnell 2016). Figure 

4-1 shows how the range of kudzu expanded 

from 1971 to 2017 as winters became less severe. 

It is likely that warming winters will also afect 

demographics and the range of other agronomic 

and invasive weeds, with subsequent impact on 

managed systems in the northern United States.

Data Sources and Limitations 

Northward migration is not universal for all weed 

species but has been observed for kudzu and other 

invasive species that can have signifcant efects 

on agriculture; such efects may include rangeland 

quality (e.g., cheatgrass) or as disease carriers 

(kudzu is a carrier for Asian soybean rust; Harmon 

et al. 2005; Ziska et al. 2011). Te University 

of Georgia is documenting and describing the 

distribution of pest and weed species across 

the United States, including kudzu (EDDMapS 

2019). However, additional eforts to document 

distribution change over time is needed.

range 1971

range expansion from 1971-2017

Chapter 4 Climate Indicators for Agriculture

28

Figure 4-1. Kudzu range in the midwestern United States, 1971–2017. 

Upper panel shows change in northern limit of kudzu, an invasive weed 

of agriculture and forests, for the midwestern United States. between 1971 

(solid line) and 2017 (dashed line). Data are updated from Ziska and 

McConnell (2016). Te Midwest was chosen to avoid major urban or 

“heat-sink” areas. 
Tere are thousands of insect species that negatively 

afect crop production. Te presence and severity of 

a pest infestation depends on geographic location 

and crop type. Insect development and distribution 

are substantially infuenced by climate. Higher 

temperatures and shifing precipitation patterns are 

expanding the incidence and range of some insect 

pests that afect agricultural production (USGCRP 

2018). One example, of many in the United States, is 

the tree loss caused by native mountain pine beetles 

that have expanded their range to higher elevations, 

have started fight a month earlier than in the past, 

and now undergo two generations per year instead 

of one (Mitton and Ferrenberg 2012). Another 

crop with pests impacted by climate change is corn. 

Corn yields are heavily impacted by insect pests, 

such as the corn earworm (Helicoverpa zea; RaeyJones 2019). Tese pests are primarily managed 

through the use of Bacillus thuringiensis (Bt) corn 

(corn that is genetically modifed to produce a 

protein toxic to the pests). Unmanaged, these pests 

damage corn plants by chewing the plant tissues, 

decreasing overall corn yield (Raey-Jones 2019). 

Additionally, corn earworm damage is associated 

with colonization of ears by mycotoxigenic fungi, 

which can lead to mycotoxin contaminated grain 

that poses a risk to animal and human health. 

Te range and life cycle of insect pests is afected 

by climate conditions, with cold winter weather 

limiting reproduction.

Importance to U.S. Agriculture

Corn earworm does not overwinter in more 

northern U.S. regions but arrives in the summer 

months (Raey-Jones 2019). Te number of 

generations completed is closely tied to regional 

climate, with northern Minnesota having only 

one generation, while warmer regions such as the 

central Great Plains have three generations per 

growing season (see discussion of insect generations 

in section 5.3). Earworm is among the most costly 

crop pests in North America, causing up to $100 

million in damage annually, with additional costs 

associated with pesticide application (Capinera 

2001). Corn earworm is also able to infest and 

reduce yield of many feld and vegetable crops, such 

as vetch, tomatoes, sorghum, and cotton, resulting 

in further crop losses (Capinera 2001).

Corn Earworm – An Example 

Difenbaugh et al. (2008) combined climate change 

projections with pest overwintering models and 

growing-degree days to estimate the future range 

of four corn pests in the United States, including 

the corn earworm. A growing degree day model 

is an index of heat accumulation calculated by 

subtracting a reference temperature that varies 

by species from the daily average temperature. 

Growing degree day values less than zero are set 

equal to zero. 

Chapter 4 Climate Indicators for Agriculture

29

4.2 Insect Infestation in Crops
Under projected future climates, it is likely that 

there will be greater degree-day accumulations, 

as well as increased winter survival, suggesting 

latitudinal or altitudinal shifs in corn earworm 

range (fgure 4-2).

Tis is particularly problematic, as the corn 

earworm has developed resistance to both 

commonly used insecticides (Jacobson et al. 2009) 

and the Cry protein in Bt corn (Pan et al. 2016; 

Dively, Venugopal, and Finkenbinder 2017). Further 

work by Deutsch et al. (2018) demonstrated that 

increases in temperature will accelerate population 

growth and metabolic rates of insect pests of 

maize, including the corn earworm, particularly 

in the temperate regions where most grain is 

currently grown. 

With respect to resistance of corn earworm, studies 

from the 20 years of Maryland pest management 

data suggest that rising temperatures can accelerate 

development of resistance to Bt. Venugopal and 

Dively (2017) found that higher-than-normal 

temperatures coupled with large acreage of Bt 

corn correlated to both higher corn earworm 

populations and crop damage. Specifcally, climateinduced changes in temperature can speed earworm 

development, increase the number of generations 

per season, and allow a larger overwintering 

population to survive (Venugopal and Dively 2017). 

Data Sources and Limitations

USDA APHIS’s Cooperative Agricultural Pest 

Survey (CAPS) conducted national and State 

suveys in collaboration with State departments 

of agriculture to facilitate early detection, rapid 

response, and management of exotic plant pests and 

has conducted surveys on exotic corn pests, such as 

European corn earworm and Asian corn earworm, 

however, the priority pests surveyed change 

annually, so not all pests have long-term datasets. 

For non-exotic corn earwom, the main limitation 

of these data sets is that the pest populations are 

monitored through statewide pest management 

programs and may thus be limited in geographic 

scope. States such as Missouri (Missouri Pest 

Monitoring Network 2018) Maryland, Michigan, 

and Virginia (Kuhar et al. 2018) have some sentinel 

plots across the State where they utilize pheromonebased traps to estimate the populations of corn 

insect pests. Terefore, the greatest challenge 

using these data is the lack of a nationwide 

sampling program that would allow national-scale 

assessment. Additionally, the diferences in Statebased data collection methods and protocols can 

lead to diferences in interpretation across States. 

Figure 4-2. Simulated changes in heat accumulation in the 21st century. 

Insects have an optimal temperature range for growth and reproduction. 

Degree days indicate the threshold temperature below which insects do not 

develop. Degree day accumulations are used to predict key steps in insect 

development. Seasonal growing degree days above 10.0°C are shown in 

panel A and above 12.7°C in panel B. GDD10 was chosen was it is the 

baseline for Northern corn earworm and GDD12.7 was chosen as it is 

the GDD based for Western corn earworm. Te more growing degree 

days above the heat threshold for development, the more favorable the 

environment for the development of corn earworm. Peak increases occur 

in the arid Southwest, the southern Great Plains, and the Gulf Coast, 

suggesting that management of corn earworm in these regions will 

become more problematic. (Figure source: Difenbaugh et al. 2008.) 

© OIP Publishing. Used by permission.

Pesticide resistance can develop faster under climate 

change, as the number of generations may increase 

within a season (Gregory et al. 2009). 

Chapter 4 Climate Indicators for Agriculture

30
4.3 Crop Pathogens

Plant diseases pose a risk to agricultural 

productivity and food security by causing reduced 

yields or inficting complete crop loss (Savary et al. 

2012). Research on the impact of climate change 

on plant diseases indicates that climate change 

conditions will alter many aspects of pathogen 

biology, including disease development or etiology, 

spatial distribution, host resistance, and plantpathogen interactions (Coakley, Scherm, and 

Chakraborty 1999; Garrett et al. 2006).

Importance of Wheat Scab 

to U.S. Agriculture

Wheat scab, caused by fungi in the Fusarium 

gramineraum species complex (sexual stage 

Gibberella zeae), is also known as Fusarium head 

blight (Parry, Jenkinson, and McLeod 1995). Past 

epidemics caused losses for the U.S. wheat and 

barley industry of $2.7 billion from 1998 to 2000 

(Nganje et al. 2004). Symptoms of the disease 

are confned to the wheat head, mainly afecting 

the grain (Wise, Woloshuk, and Freije 2015). 

In addition to grain losses due to infection, the 

pathogen is widely recognized as afecting wheat 

quality through mycotoxin production. Such 

contamination can preclude the use of afected 

wheat for animal feed and human food products 

(Parry, Jenkinson, and McLeod 1995). For example, 

the mycotoxin deoxynivalenol (DON), also known 

as vomitoxin, is problematic as animal feed and in 

processed food products and the food supply chain 

(Rotter 1996). Due to the importance of the disease 

in direct loss to crop yield and indirect yield loss due 

to mycotoxin contamination, steep price discounts 

of contaminanted grain, and increased risks for 

producers and grain merchandisers including 

traders and processors, this disease risk is predicted 

through the Wheat Scab Forecasting Model (Wilson 

et al. 2017). 

Wheat scab is one of the few plant diseases to have 

a robust nationwide predictive tool. Te Wheat 

Scab Forecasting Model, currently being applied in 

30 States, uses relative humidity within a region to 

help predict the risk of development of wheat scab 

throughout the growing season, so growers can make 

decisions about fungicide applications (Fusarium 

Head Blight Prediction Center, n.d.). According to 

De Wolf et al. (2019), this prediction efort includes 

web-based tools, which display daily estimates of 

disease risk for 30 States. Commentary developed by a 

disease specialist in each State is displayed along with 

the risk maps. Commentary is also distributed via an 

FHB Alert System that sends email and text messages 

to mobile devices. Extended periods of high relative 

humidity just before or during fowering favor disease 

development (Shah et al. 2013). Humidity at 15 days 

prior to fowering is used to predict the risk of severe 

disease (De Wolf, Madden, and Lipps 2003). 

Research, modeling, and observations over the 

past decade have revealed a number of linkages 

between climate conditions and the development 

and severity of wheat scab. Te Wheat Scab 

Forecasting Model has been used to examine how 

diferent scenarios of future climate change could 

afect the extended periods of high humidity that 

favor disease development. Multiple risks have been 

identifed. Specifc isolates of the fungus can produce 

more mycotoxins in response to both colder- and 

warmer- than- normal temperatures (Vujanovic, 

Goh, and Daida 2012). Rising CO2 increases both 

disease severity and DON contamination (Váry et al. 

2015). Regions with wetter springs are expected to 

experience increased disease (Vaughn, Backhouse, 

and Del Ponte 2016). Taken together, these results 

show that the precipitation, temperature, and 

humidity indicators discussed in sections 2.1, 2.3, 

and 2.5, respectively, are relevant to wheat scab 

risk; conversely, the rising incidence of wheat scab 

shows how such physical changes are afecting U.S. 

agriculture.

Upper left - early scab infection on bottom surface 

of leaf; upper right - late season scab with top surface 

lesions; lower left - early scab on fruit; lower right - 

late season scab.

Chapter 4 Climate Indicators for Agriculture

31
Plant Pathogen Distribution – An Example

Te largest challenge with determining how climate is impacting the distribution of plant pathogens and 

pests is access to long-term databases on the presence or distribution of crop pathogens. Work by Bebber, 

Ramotwoski, and Gurr (2013) looked through the historical databases of the CABI Distribution Maps of Plant 

Diseases (CABI 2020a) and CABI Distribution Maps of Plant Pests (CABI 2020b) to assess whether they were 

changes in the geographic distribution of plant pests and diseases since 1960. To conduct this analysis, the 

authors found the latitudes and earliest recorded dates of 612 crop pests and pathogens which are reported 

at either a regional or country level. It should be noted that this was observational data only, leading to an 

observational bias of reporting diseases that are present at a larger scale and cause economically signifcant 

losses. Bebber, Ramotowski, and Gurr (2013) diferentiated the data based upon the fve main taxa that cause 

plant disease: bacteria, fungi, nematode, oomycetes, and viruses. Trough their analysis, the authors found 

that the increasing temperatures have caused a poleward shif of most pathogen taxa of nearly three kilometers 

a year, on average, since 1960 (Bebber, Ramotowski, and Gurr 2013). With the exception of nematodes, all 

other main taxa that cause plant diseases have a distribution that is moving farther away from the equator 

as temperatures are increasing (as shown below in fgure 4-3). Tis shif is indicative of changes in disease 

distribution, meaning that pathogens are being found in new locations. 

-1,000

1,000

-500

0

500

1960 2000

Bacteria Fungi Viruses Oomycota Nematoda

Distance from the equator

Year

1980 1960 1980 2000 1960 1980 2000 1960 1980 2000 1960 1980 2000

Data Sources and Limitations

Te U.S. Wheat and Barley Scab Initiative 

(USWBSI) coordinates research between Federal, 

State, and private-sector scientists who work 

closely with stakeholders across the food chain 

to ensure a safe wheat supply from feld to fork. 

Tis consortium consists of over 20 land-grant 

universities, the USDA Agricultural Research 

Service (USDA ARS), and the International Maize 

and Wheat Improvement Center (CIMMYT). Te 

challenge in utilizing crop pathogens as an indicator 

is that the majority of pathogens lack a nationwide 

monitoring program. Only a few diseases, such 

as wheat scab, have the necessary data available. 

Te National Plant Disease Diagnostic Network 

(NPDN) is a consortium of plant diagnostic 

labs established in 2002 to enhance agricultural 

biosecurity. Tis consortium, supported by funding 

through USDA National Institute of Food and 

Agriculture (NIFA), uploads disease diagnoses 

Figure 4-3. Distance from the equator over time for fve major types of plant pathogens. 

Panels represent distance from the equator for fve major groups of pathogens that cause disease in plants (bacteria, fungi, viruses, fungal-like oomycota, 

and nematoda). Due to the consistency of annual climate patterns at the equator, the highest diversity of plant pathogens is found at equatorial latitudes. 

As global temperatures have increased, some pathogens (bacteria, fungi, oomycotaa, and viruses) have been found at increasing distances from the equator 

since 1960, while nematodes show the opposite trend of being found closer to the equator (Bebber et al, 2013).

into a national database. However, these data are 

currently not publicly accessible, beyond USDA 

APHIS (Animal and Plant Health Inspection 

Service) quarantine reporting, and only include 

samples that are sent to plant disease clinics. In 

addition, not all of the diseases present are sent to 

clinic for diagnosis, and extension agents and crop 

consultants might not be able to diagnose samples 

in the feld. 

Te main challenge with assessing the efects of 

climate on crop pathogens is that there is no global 

database that tracks disease beyond observational 

data, such as the CABI databases discussed above. 

Caravajal-Yepes et al. (2019) calls for the need to 

develop a global surveillance system to track the 

movement and spread of crop disease for major 

staple crops. Such a system would develop and 

support the long-term database of disease outbreak 

necessary to determine how climate is changing 

where, when, and what diseases are occurring. 

Climate Indicators for Agriculture

32
4.4 Pesticide Use 

Climate change is likely to alter the geographic 

ranges and impacts of a variety of insect pests, 

plant pathogens, and weeds, and the consequences 

for managed systems, particularly agriculture, 

remain uncertain. Tis uncertainty is related, 

in part, to whether pest management practices 

(e.g., biological, chemical, cultural) can adapt to 

climate/CO2-induced changes in pest biology to 

minimize potential loss. Te ongoing and projected 

changes in CO2, the environment, managed plant 

systems, and pest interactions necessitate an 

assessment of current management practices and, 

if warranted, development of viable alternative 

strategies to counter damage from invasive alien 

species and evolving native pest populations. At 

present in the United States and other developed 

countries, these pests are managed primarily 

through chemical means with pesticides (USGS 

2018). Pesticide usage is complex and can depend 

on a number of environmental factors. However, 

research has demonstrated signifcant shifs in pest 

populations as temperatures warm, particularly 

winter temperatures (Porter, Parry, and Carter 

1991; Harvell et al. 2002). Overall, changes in 

pest demography and enhanced reproduction 

with warming are projected to result in increased 

pesticide application (Delcour, Spanoghe, and 

Uyttendaele 2015). In addition, it has been shown 

in a number of studies that rising CO2 may also 

diminish herbicide efcacy (Ziska 2016). 

Importance to U.S. Agriculture

Pesticide use is widespread in U.S. agriculture. 

For example, at present, approximately 95 percent 

of all soybean in the United States is Roundup 

Ready, meaning that most producers are relying on 

chemical control for pest management. Changes in 

pesticide usage can carry economic, environmental, 

and potential health costs. Greater pesticide 

applications afect proft margins and can infuence 

the rate of pesticide resistance and increase longterm management costs. Such costs may also 

include exacerbation of environmental impacts and 

efects on public health. 

Chapter 4 Climate Indicators for Agriculture

33
Fungicide and Insecticide Applications to 

Soybean – An Example

Using data from USDA NASS, a north–south 

transect of soybean-growing States in the Midwest 

was examined from Minnesota to Louisiana 

between 1999 and 2013 (Ziska 2014). Teoretically, 

as temperatures warmed from north to south, crop 

losses from pests should have risen. However, no 

diferences in the average soybean yield per acre 

were noted. Tis may have been compensated for, 

in part, by additional pesticide use; there were 

signifcant increases in pesticide use from north to 

south (e.g., fungicide and insecticides; see fgure 

4-4). Tese diferences corresponded to increasing 

minimum winter temperatures and changes in 

pest demographics, including pest intensifcation. 

For example, in the southernmost locations, such 

as Louisiana and Mississippi, there was higher 

coverage by perennial weeds relative to the northern 

sites. Perennial weeds are, in general, more difcult 

to chemically control. 

Warmer winter temperatures and reduction of 

frost-imposed limitations on pest populations are 

very likely to increase pest populations. Tis may 

result in greater reliance on pesticide application to 

maintain maximum production from crop systems 

like soybean. For example, soybean yields per 

acre for Roundup Ready soybean are the same in 

Minnesota as they are in Louisiana; the diference 

is that with warmer winters (e.g., in Louisiana), 

pesticide applications are much higher. Increased 

pesticide use, in turn, may carry additional 

economic or environmental costs. Tese data 

suggest that as winters warm, pesticide use (e.g., 

herbicide, insecticide, and fungicide) may increase 

to compensate for increased pest pressures and 

shifing populations. However, additional work 

is needed to verify these trends.

Figure 4-4. Change in application of pesticide for genetically modifed 

soybean as a function of minimum temperatures.

Higher minimum temperatures consistent with warmer winters projected 

under climate change may lead to increased application of insecticide and 

fungicide. Data are from Ziska (2014).

Data Sources and Limitations 

Research results show a relationship between 

warmer temperatures and pest infestation. 

Warmer temperatures appear to result in increased 

application of pesticide, but reduced pesticide 

efcacy can also increase pesticide application 

rates. In addition, it has been shown in a number 

of studies that rising CO2 may also negatively afect 

herbicide efcacy (Ziska 2016). Specifcs as to how 

increasing temperature could infuence pesticide 

efcacy, however, require additional information 

about which pesticides are used, their strength, 

and when they are applied. In addition, rates of 

resistance to a given pesticide increase with usage, 

and whether there is a temperature element to 

increased pesticide resistance also needs to be 

clarifed.

Chapter 4 Climate Indicators for Agriculture

34
Phenology describes the timing of phenomena 

related to the seasonal cycle of plant and animal 

life. It can encompass a broad range of events, 

including plant bloom, insect emergence, and 

animal hibernation (Schwartz 2003). Many of these 

occurrences are infuenced by changes in climate, 

especially temperature. Terefore, phenological 

variables are ofen used to monitor the progress 

of biological events within a particular year or to 

identify longerterm interannual cycles or trends. 

Many phenological events are quite visible, like 

the bloom of lilacs in the spring or the sudden 

appearance of beetles in the garden. Tis makes 

phenological occurrences unique indicators of 

climate change (Schwartz, Ahas, and Aasa 2006). 

Tey are also readily observed by the public, 

making it easier to communicate changes and 

impacts. 

Phenological indicators can be used to assess the 

sensitivity of a particular organism to climate 

change (e.g., Tackeray et al. 2016). For instance, 

the emergence of insects is a phenological indicator 

that is heavily infuenced by temperature (Taylor et 

al. 2018). Tis indicator not only describes when a 

particular species might emerge, but also how many 

generations might occur during a growing season. 

It can also help identify efects related to climate 

change, such as lower agricultural yield, poorer fruit 

quality, and increased pesticide use. 

PHENOLOGICAL

INDICATORS

CHAPTER 5 Climate Indicators for Agriculture

35

Another strength of phenological indicators is their 

ability to integrate climate conditions over time 

periods from days to years. Te bloom dates of fruit 

trees are an example. Bloom is the culmination of 

heat accumulated over the preceding months, and 

long-term records of bloom dates can be crucial in 

identifying decadal trends (DeGaetano 2018). In 

many applications, growing degree days (typically 

over 50°F or 40°F) provide the bridge between a 

purely climate-related indicator like temperature 

and the biological event, such as bloom or 

emergence. 

Te science of phenology is well established, with 

the relationship between climate and biological 

phenomena extensively documented in the 

literature. However, most U.S. phenological data 

sources are not widely available, particularly 

ones having a long historical record, ample and 

consistent geographic coverage, and ongoing 

observation. Such direct observations are a 

necessary data source for ensuring the reliability 

of the modeled relationships under future climate 

conditions. Statewide crop development time series 

are available from the USDA National Agricultural 

Statistics Service at https://www.nass.usda.gov/

Charts_and_Maps/.
Te following phenological indicators were selected 

for this report: 

• Timing of budbreak in fruit trees

• Pollinators and pollinator management

• Winter chilling units

• Insect generations per season

• Disease vectors in livestock 

Phenological indicators provide a measure of the 

impact of climate change on plants and animals. 

In some cases, long time series of observations 

of readily identifed biological phenomena, such 

as plant bloom, provide a direct indicator of a 

changing climate’s infuence. Spring budbreak in 

apple trees is such an indicator highlighted in this 

report. In other cases, indirect indicators are used 

to assess the impact of climate. Tese indirect 

measures use well-established models to relate 

climate to the biological impact being assessed. 

In this report, indicators representing the number 

of insect generations per season and winter chilling 

fall into this category. 

In general, the phenological indicators in this report 

sample the diferent types of phenological indicators 

that can be used to monitor changes in the climate. 

Tey cover the spectrum from long-term time 

series of bloom dates to model-based estimates of 

insect emergence to broader observations of the 

changes in the spatial range of certain species. Te 

highlighted indicators are by no means intended to 

be an exhaustive list or even a compilation of the 

most important phenological indicators. Rather, 

they illustrate that phenological indicators can 

represent diferent facets of agriculture and have 

regional as well as national applicability.

Largely the phenological indicators show that in 

a warming world, seasonal phenological cycles are 

accelerated, leading to earlier bloom, earlier and 

more frequent insect emergence, and a generally 

northward expansion of species ranges. By 

monitoring these and other phenological indicators, 

farmers and ranchers will be better able to anticipate 

climate-related changes; the timing of crop, weed, 

and insect development; changes in invasive 

species ranges; and revisions to crop management 

scheduling.

CHAPTER 5, cont. Climate Indicators for Agriculture

36
Budbreak (the initiation of bud growth) and bloom 

dates are unique climate change indicators that 

integrate multiple weather-related factors over time. 

Although infuenced primarily by temperature, the 

dates can also be indirectly afected by soil moisture, 

solar radiation, and humidity. Winter temperatures 

infuence the date on which bud development can 

commence, and subsequent temperatures drive the 

rate of bud development and fowering in spring 

(Rigby and Porporato 2008). Across the United 

States, winter and spring temperatures have warmed 

since the 1970s (USGCRP 2017), presumably 

leading to the trends toward earlier bloom dates 

that have been widely reported for domestic fruit 

crops (e.g., Legave et al. 2013; Wolfe et al. 2005).

For many crops and natural species, direct 

observations of key bud stages exist. Te historical 

lilac and honeysuckle observations that are a part 

of the National Phenology Network are perhaps the 

most extensive collection of phenology observations 

in the United States (Schwartz, Betancourt, and 

Weltzin 2012). 

Te collection of both long-term lilac bloom 

data and similar data for key fruit species has 

also enabled degree-day-based models of bud 

development to be developed (e.g., Eccel et al. 

2009). Degree days represent the cumulative sum 

of temperatures above a threshold related to bud 

development. Such model-derived information 

is commonly used in agricultural weather 

networks, such as the Network for Environment 

and Weather Applications (NEWA), AgroClimate 

(FL), Enviroweather (MI), and AgWeatherNet 

(WA), to forecast and monitor bud development. 

Such models are useful proxy indicators for direct 

observations of bud development indicators.

Importance to U.S. Agriculture

Te cumulative efect of mild late-winter and 

early-spring temperatures ofen leads to early leaf 

emergence and bloom, leaving critical development 

stages vulnerable to subfreezing temperatures later 

in the spring (Wisniewski, Artlip, and Norelli 2016). 

Warm winter temperatures paradoxically increase 

the risk of spring freeze injury, as accelerated 

development may not outpace the decline in 

the probability of subsequent below-freezing 

temperatures (Rosenzweig et al. 2011; 

Gu et al. 2008). 

Chapter 5 Climate Indicators for Agriculture

37

5.1 Timing of Budbreak in Fruit Trees
Apple Bloom Dates in New York – 

An Example

New York State is the Nation’s second-largest apple 

producer, with over 29.5 million bushels produced 

annually (USDA NASS 2019a). In fgure 5-1, 

phenology data collected at the Cornell University 

Hudson Valley Farm near Poughkeepsie, New 

York, show that since 1980 the dates of the key 

phenological stages that occur during the early 

(greentip), intermediate (tight cluster), and late 

(full bloom) stages of the McIntosh apple bud 

development cycle have gotten earlier by 4 to 7 

days. Tis pattern is also replicated by phenological 

models using degree days. Te observed pattern 

toward earlier budbreak stages is expected to 

continue through this century, according to climate 

model projections (Wolfe et al. 2018).

Data Sources and Limitations

Although direct observations of bud development 

in fruit species, such as apples and grapes, have 

been made over time, they tend to exist as 

individual isolated data sets. Te observations from 

the Cornell University Hudson Valley Farm are 

available online (Cornell University 2019). Such 

data are useful indicators at local or State levels, 

but lack of aggregation complicates their use at a 

national scale. Alternatively, phenological stages 

can be estimated based on more readily available air 

temperature observations. However, these empirical 

relationships are based on historical weather 

conditions. Teir application under future climate 

conditions, which are very likely to be substantially 

diferent, could be problematic. 

1980

May-15

May-1

Apr-15

Apr-1

1985 1990 1995 2000 2005

1980 2000 2020 2040 2060 2080

2010 2015

Date

Jun-1

May-15

May-1

Apr-15

Apr-1

Mar-15

Year

Date

Year

Greentip Tight Cluster Full Bloom

Greentip

Tight Cluster

Full Bloom

Figure 5-1. Historical and projected changes in apple bud development 

stages. 

Te upper graph shows the observed apple bud development dates at 

Poughkeepsie, New York, for greentip (green line), tight cluster (blue line), 

and full bloom (red line). Te dotted horizontal lines show the average 

bud development dates observed in 1980–1995 and 1996–2013. Te lower 

graph shows changes in the date of occurrence of the green tip (green line), 

tight cluster (blue line), and full bloom (red line) stages of apple blossom 

development based on the average of 10 downscaled climate model 

simulations for a grid point near Geneva, New York, based on RCP 8.5, 

a high-emission scenario. Dots illustrate the year-to-year variation of the 

modeled dates; lines sho a 5-year running mean to highlight longerterm 

trends. Adapted from Jentsch et al. 2018. Photos from Chapman and 

Catlin 1976.

Chapter 5 Climate Indicators for Agriculture

38
Native and managed pollinators, such as bees, 

wasps, and butterfies, represent a biological 

necessity for agriculture. Phenology of wild bees is 

related to annual cycles that include either a larval 

or adult diapause before spring emergence. For 

some species, such as the solitary orchard pollinator 

Osmia lignaria, researchers have investigated the 

wintering regimes necessary to time emergence of 

bees needed to pollinate diferent crops, ranging 

from almonds in February to apples in May (Bosch 

and Kemp 2003). For the honey bee, Apis mellifera, 

spring emergence is related to spring temperature, 

with emergence occurring earlier with the warmer 

temperatures associated with changing climate 

(Gordo and Sanz 2006). Bloom dates (discussed 

in section 5.1 on budbreak) are infuenced by 

temperature and other climate factors, including 

soil moisture and humidity (discussed in sections 

2.2 and 2.5, respectively). Tere is evidence that 

climate change is leading to changes in both bloom 

dates and the timing of pollinator emergence (Wolfe 

et al. 2018; Gordo and Sanz 2006). Bloom dates in 

apples, for example, have advanced by 4 to 7 days 

since the 1980s, as discussed in the previous section. 

Chapter 5 Climate Indicators for Agriculture

39

5.2 Pollinators and Pollinator Management
Importance to U.S. Agriculture 

For pollination to occur, adult bees that are capable 

of foraging must emerge with fower emergence. 

Divergence of bloom dates and pollinator 

emergence dates would pose a signifcant challenge 

for agricultural production. For example, pollinators 

must emerge in synchrony with bloom dates for 

successful fruit set to occur. While a trend of earlier 

fowering is widely reported in the literature (Legave 

et al. 2013), there is less research on pollinator 

phenology as it relates to climate change. Te 

emergence dates of spring-active insect taxa have 

been identifed as a good indicator of response to 

climate change (Bartomeus et al. 2011). Tracking 

these dates and any divergence between them and 

bloom dates would enable producers to assess risks 

to pollination. 

Bee Emergence Dates in the Northeastern 

United States – An Example 

Bartomeus et al. (2011) evaluated the rate of change 

of emergence of native bees in the northeastern 

United States as it relates to changes in spring 

temperatures resulting from climate change. For 

the 10 native bee species they studied, they found 

that the phenology has advanced by a mean of 

10.4 ± 1.3 days. While the researchers looked at 

pollinator phenology over a period of 130 years, 

they found that approximately 70 percent (7.2 ± 1.2 

days) of this advance in emergence has taken place 

since 1970. Te advance in pollinator phenology 

directly parallels the increase in regional spring 

temperatures. Te authors also found that changes 

in bloom date for plants were advancing at the 

same rate as those of the 10 bee species studied and 

that the emergence of the bee species studied were 

correlated to changes in plant fowering dates. In 

summary, climate change afected the phenological 

changes in bees and plants at the same rate during 

the period studied. However, the authors noted that 

phenological mismatch could occur if warming 

continues at the rapid rate observed during recent 

decades. Figure 5-2 illustrates bee population levels 

as they relate to temperature over time. 

Figure 5-2. Te impact of April temperatures on bee emergence. 

Panel A shows a scatter plot of the collection days of the bee specimens 

studied from the museum samples used in the research. Panel B indicates 

the mean temperature in April at the locations where the samples were 

obtained. In both panels, the black line is the overall trend while the 

red line is the trend from 1970 to 2010. Panel C shows the correlation 

between the date of collection and mean average April temperatures 

of the collection location of each specimen. Earlier collection dates 

occur under warmer mean April temperatures. Te remaining panels 

are representative samples of the species used in the study: (D) Apis 

miserabilis, (E) Colletes inaequalis, (F) Osmia lignaria, and (G) Bombus 

impatiens queen. (Figure source: Bartomeus et al. 2011.) Collection day Mean April temperature Collection day 100 150

April Temperature (oC)

5

1880 1900 1920

10 15

200 250

1940 1960 1980 2000

5

10

15

100

150

200

250 A

B

C

Year

Data Sources and Limitations

Bartomeus et al.’s (2011) data for pollinator 

emergence are based on contemporary data 

(2000–2010) in conjunction with data recorded 

for museum specimens dating back to the 1880s. 

Museum specimen records note whether a species 

was in fight on the given collection date to 

extrapolate the span of activity for a given species 

in a given year. Details on these data are available 

online (Bartomeus et al. 2011). 

Chapter 5 Climate Indicators for Agriculture

40
5.3 Winter Chill Units

A chill unit is a measure of a plant’s exposure to 

cool temperatures during winter. Several methods 

exist for computing this unit, with biological 

activity assigned to diferent levels of temperature 

(Luedeling 2012). A common method, widely 

used throughout the United States, is known as 

the “Utah” model (Richardson, Seeley, and Walker 

1974). In the Utah model, optimal chilling occurs 

for temperatures between 37°F and 48°F. Each 

hourly temperature in this range contributes one 

chill unit to the seasonal total. Slightly colder, 

35°F–36°F, and warmer, 48°F–54°F, temperatures 

contribute one-half chill unit. Temperatures below 

35°F are too cold and temperatures above 54°F are 

too warm to contribute to the chill requirement. 

Most fruit trees, particularly stone fruits such 

as peaches, apples, and nuts, require a period of 

chilling during winter for bloom to occur during 

spring (Rai et al. 2015). Exposure to a prolonged 

period of cool temperatures is required before buds 

can develop as temperatures warm in the spring. 

Te required chilling period varies among species 

and within a particular species among cultivars 

(Baldocchi and Wong 2008). Some cultivars of 

apricots, cherries, and plums require less than 

300 hours of chilling, while many types of apples, 

especially those that fourish in colder climates, 

require more than 1,000 chill units. During winters 

without an adequate period of chilling, spring 

bloom is unreliable and delayed. Fruit set and 

quality are also adversely afected (Darbyshire, 

Measham, and Goodwin 2016). Winter chilling 

is also a factor for crops such as winter wheat 

(Chouard 1960).

Importance to U.S. Agriculture

Over the past several decades, temperatures across 

much of the United States have warmed. In many 

places, the greatest rate of warming has occurred 

in winter (USGCRP 2017). With this warming 

comes the possibility of failing to meet the required 

number of chilling hours (e.g., Houston et al. 2018). 

Tis is particularly true in southern locations and 

in the mild west coast climates. In these locations, 

warmer winter temperatures can prevent attainment 

of optimal chilling levels, leading to production 

losses. By 2050, projected winter chilling conditions 

will no longer support some of California’s main 

fruit crops, leading growers to consider either 

diferent varieties or applying chemicals to induce 

the end of dormancy (Luedeling, Zhang, and 

Girvetz 2009). In more northern climates, the 

risk of not attaining the necessary chill is not 

as problematic. However, warming afects the 

timing of meeting the seasonal chill requirement. 

If sufcient chill levels are reached too early in 

the winter, buds can break dormancy too soon 

in spring, increasing the risk of injury from 

subfreezing temperatures.

Chapter 5 Climate Indicators for Agriculture

41
Chill Units in the Central Valley of California – An Example

Figure 5-3 shows seasonal maximum chill units for three locations spanning California’s Central Valley. Orland 

is in the north, where the main crops are almonds and walnuts. Te area around Modesto, in the central part 

of the valley, grows stone fruits (apricots, peaches, and cherries), in addition to walnuts and almonds. Farther 

south near Bakersfeld, cherries, blueberries, and nuts are important crops. Chill units have declined at each 

site at a rate of between 20 and 50 chill units per decade. At Orland, 30 of the past 35 years have accumulated 

fewer chill units than the 1961–1990 average, while at Modesto, 27 of the past 35 seasons have seen fewer chill 

units than were common during 1961–1990. At all sites, the past 7 years have consistently seen below-average 

chill accumulation, a unique feature in the historical record of these stations.

Data Sources and Limitations

Chill units are a fairly robust indicator of climate 

change. Teir computation is based on a single 

variable, temperature, which has a long historical 

observation record. Nonetheless, this index is not 

without its limitations. First, hourly data provide 

the optimal basis for computing this indicator, 

but records of such data from many agricultural 

monitoring networks do not extend further back 

than about 1990. (Te longest records of these 

data are from airport locations and city centers.) 

Figure 5-3. Departure of annual chill unit accumulation from the 1961–1990 average at three locations in California’s Central Valley. 

Red bars indicate lower-than-average chill unit accumulation and blue bars indicate greater-than-average chill unit accumulation. Te thin vertical line 

demarks the 35 years prior to 2018. (Data source: Menne et al. 2012a)

But daily maximum and minimum temperature 

observations can be used in conjunction with 

statistical methods to ft the hourly pattern of 

temperature (Linvill 1990). 

A second trait of this indicator is that there is not 

a standard defnition for chill units. Methods like 

the Utah method, used here, are the most widely 

used. More recent research has introduced the idea 

of chilling portions, based on the dynamic model 

(Erez et al. 1990). It is particularly well suited for 

warmer climates but has not been widely adopted 

(Zhang and Taylor 2011).

Chapter 5 Climate Indicators for Agriculture

42
Insects are most likely to be early sentinels of 

increasing temperature, as temperature is highly 

correlated with insect emergence and reproduction 

(Taylor et al. 2018). Changes that afect insect 

reproduction can, in turn, alter their phenology, 

distribution, and evolution, with consequences 

for development, survival, and range. Tree or 

four generations of some agriculturally important 

pests can emerge in a single growing season before 

declining day length signals a period of suspended 

development known as diapause. Warming climate 

conditions can result in an additional generation of 

pests during the growing season (Tobin et al. 2008).

Insect emergence can be monitored directly or 

inferred based on the accumulation of heat units 

(e.g., Jones and Duckworth 2018). A growing 

degree day model is an index of heat accumulation 

calculated by subtracting a reference temperature 

that varies by species from the daily average 

temperature. Growing degree day values less than 

zero are set equal to zero. 

Jyoti, Shelton, and Barnard (2003) described a 

typical growing degree day model. In the model, 

half of the overwintering generation emerges 

following the accumulation of 808 base 40°F 

growing degree days starting from January 1. 

Subsequent generations that did not overwinter 

emerge following the accumulation of 1,733 (frst 

generation), 2,560 (second generation), and 3,279 

(third generation) growing degree days. If enough 

growing degree days accumulate before the onset 

of diapause, a fourth generation can also emerge 

(Johnsen and Gutierrez 1997). 

Importance to U.S. Agriculture

Warming temperatures and additional insect 

generations increase the pest pressure on 

agricultural crops (Porter, Parry, and Carter 

1991). Hirschi et al. (2012) argued that the routine 

emergence of a third generation of codling moth, 

an economically important pest in apples, is likely 

under warmer climate conditions. Taylor et al. 

(2018) indicated that a lengthening growing season 

will increase the number of potato leafopper 

generations in the Midwest. Wolfe et al. (2018) 

cited increased pesticide applications as a likely 

adaptation for this increased insect pressure 

(pesticide use as an indicator is discussed in 

section 4.4). Such an increase might stimulate the 

development of insecticide resistance (e.g., Huseth 

et al. 2018). Taylor et al. (2018) also noted the likely 

increase in pesticide use will increase the carbon 

footprint of agriculture.

Chapter 5 Climate Indicators for Agriculture

43

5.4 Insect Generations per Season
Data Sources and Limitations

Climatologically, growing degree day–based 

indicators of insect emergence are straightforward 

to compute, as the only input needed is daily 

average temperature (the average of the daily 

maximum and minimum). Tese data are readily 

available from robust data sources, such as the 

Global Historical Climatology Network (GHCN) 

maintained by NCEI (Menne et al. 2012a). 

Te direct observation of insect emergence from 

scouting or trap-catch data could be considered as 

a complementary indicator. Tis would eliminate 

the uncertainties associated with using an empirical 

model such as that of Jyoti, Shelton, and Barnard 

(2003), which was developed and validated under 

past climate conditions that might not refect the 

conditions likely to be experienced in the future.

Seasonal degree day accumulation Trend from 1948-2018

3000

1948 1958 1968 1978 1988

Year

1998 2008 2018

Base 40 degree day accumulation 3200

3400

3600

3800

4000

4200

4400

4600 Figure 5-4. Number of insect generations 

per season based on seasonal growing 

degree day accumulations. 

Te graph shows the seasonal (January 

1 to September 30) base 40°F growing 

degree day accumulations for the Ithaca, 

New York, GHCN station. Growing 

degree day thresholds associated with 

the emergence of the third and fourth 

nonoverwintering generation of cabbage 

maggot are indicated by transitions in 

background shading from blue to yellow 

for the third generation and yellow to red 

for the fourth generation. More insect 

generations are observed during warmer 

growing seasons. (Source: Menne et al. 

2012a).

Number of Seasonal Cabbage Maggot Generations in New York – An Example

Te cabbage maggot is an insect that afects cruciferous crops, such as cabbage, broccoli, and radishes. 

Larvae damage the roots and stems of these crops, either reducing yields or making the vegetable 

unmarketable (particularly radishes). In New York, typically three nonoverwintering generational cycles 

occur during the year. To the south, in Pennsylvania, four nonoverwintering generations can emerge.

Seasonal accumulations of base 40°F growing degree days from January 1 to September 30 for Ithaca, New 

York, are used as input to the Jyoti, Shelton, and Barnard (2003) model to determine the maximum number 

of possible generations in each season (fgure 5-4). Prior to 1991, the accumulation of enough growing degree 

days to allow for the emergence of a fourth nonoverwintering generation was rare, occurring in only 7 percent 

of these years. Te 3 years prior to 1991 in which the threshold was exceeded all occurred in the 1950s. Since 

1991, the accumulation of growing degree days has increased considerably. In the 27 years from 1991 to 2017, 

33 percent of the seasons accumulated enough growing degree days for a fourth nonoverwintering generation 

to occur. From 1950 to 2017, seasonal growing degree days accumulation increased at a rate of approximately 

1 percent per year, for an increase of 40 growing degree days over that period. 

Chapter 5 Climate Indicators for Agriculture

44
Tick-vectored diseases of cattle can decrease 

fertility, body weight, and milk production. 

Examples of economically signifcant tickvectored cattle diseases are babesiosis, theileriosis, 

and anaplasmosis. Management of these diseases 

is difcult due to the rapid development of 

resistance to acaricides, pesticides used to manage 

ticks, in tick populations. Overall, climate change 

is increasing the prevalence, biodiversity, and 

range of tick populations and the tick-associated 

diseases they spread. Tis is particularly 

problematic, as tick-borne cattle diseases are most 

prevalent in developing countries in tropical and 

subtropical regions.

Importance to U.S. Agriculture

Te cattle fever ticks Rhipicephalus annulatus and 

R. microplus vector the protozoan parasites of 

the genus Babesia that cause bovine babesiosis, 

commonly known as cattle fever. Cattle fever 

ticks and babesiosis were introduced to North 

America in the early 1700s, through Spanish 

colonists’ unknowingly bringing infested cattle 

and horses (USDA APHIS 2010). Te Cattle Fever 

Tick Eradication Program (CFTEP; USDA APHIS 

2018) was initiated in 1906 by USDA APHIS to 

quarantine and eradicate babesiosis. Tis program 

was successful in nearly eradicating the ticks and 

disease by 1943, except in the permanent quarantine 

bufer zone, which is currently roughly 2,220 km2 in 

Texas along the Mexico border. 

Chapter 5 Climate Indicators for Agriculture

45

5.5 Disease Vectors in Livestock
Suitability

1 2 3 4 5 6 7 8 9 10 N

2050

Present

Figure 5-5. Present and future (2050) geographic areas in the United 

States with suitable habitat for cattle tick.

Te fgure shows the predicted areas in the United States with suitable 

habitat for R. annulatus cattle ticks based upon model projections. A 

suitability of zero occurs when none of the models fnd suitable tick 

habitat, while a suitability of 10 occurs when all of the models agree that a 

habitat would be suitable. Te top panel habitat that is currently suitable, 

and the bottom panel represents projected suitable habitat in 2050, 

demonstrating that the habitat for the tick will increase under projected 

climate conditions. Figure from Giles et al. (2014), originally published in 

Parasites & Vectors. 

However, there are concerns that the CFTEP 

may not be adequate for controlling the spread of 

other tick-borne diseases in the future as climate 

continues to change. One of the main challenges 

in managing ticks in livestock is that the ticks 

readily move between domesticated livestock and 

wildlife, such as deer. Modeling by Giles et al. 

(2014) demonstrated that projected increases in 

temperature could result in an increased range of 

the cattle fever tick species and that this has the 

potential to reintroduce the tick and babesiosis into 

major cattle-producing regions of the United States. 

Cattle Fever Tick – An Example 

Pérez de León et al. (2012) identifed a 30-year 

cyclical pattern to the tick infestation in southern 

Texas and suggested that it could be driven by the 

El Niño–Southern Oscillation. Giles et al. (2014) 

combined a decade of USDA APHIS and ARS cattle 

fever tick monitoring records for historical data. 

Observations showed an increase in tick infestations 

outside of the quarantine area. Tey also used 

ecological models and climate projections to explore 

how climate change might afect the range of ticks 

in the future, fnding that an increase of 2°C–3°C 

in annual mean temperature would expand the 

potential range of R. microplus ticks across Texas 

and the southern United States, including Florida 

(Giles et al. 2014). A map of suitable habitat 

range for ticks in 1906, present-day, and in 2050 is 

presented in fgure 5-5.

Data Sources and Limitations

Te CFTEP has over a decade of data on the 

distribution of cattle fever ticks in the United 

States. USDA APHIS collects data on both the 

population and distribution of the ticks on an 

annual basis. Continued tracking of tick population 

and distribution in the context of climate change 

will provide producers with up-to-date information 

on the changing risks of tick-borne diseases. Such 

information could contribute to better management 

and prevention of outbreaks.

Chapter 5 Climate Indicators for Agriculture

46
SOCIOECONOMIC

INDICATORS

Te infuence of climate change on agricultural 

production extends beyond the biophysical issues 

discussed in previous sections. Climate change 

and the biophysical changes caused by climate 

change also afect the agricultural workforce, 

operational costs of producers, food storage and 

transportation, commodity prices, the food prices 

paid by consumers, and food security (Brown et al. 

2015). Tracking such changes in prices and costs 

could, in principle, provide information about the 

impacts of climate change on agriculture. However, 

climate change is ofen one of many factors, making 

it difcult to quantify the efects caused by climate 

change alone. In addition, much information about 

the costs borne by producers is proprietary and/or 

not publicly available. 

Food prices are a good example of this difculty. 

Tere is strong historical evidence that extreme 

weather and climate conditions have led to periods 

of reduced supply and increased prices of some 

commodity crops (Trostle et al. 2011). Many 

economic studies show that climate change is 

likely to exert upward pressure on prices in the 

future (Brown et al. 2015). But it is very difcult 

to attribute specifc observed increases in food 

prices to climate change alone when fuctuations 

in consumer preference and demand; tax and trade 

policies; changing prices of fuel, fertilizer, and other 

inputs; and many other factors also play a role in 

determining food prices at any given time. Tese 

attribution challenges currently limit the utility of 

price information as an indicator of climate impacts.

Tere are some socioeconomic aspects of 

agricultural production that are better suited as 

indicators because the infuence of climate change is 

more clearly a dominant cause of observed changes, 

observations have been made over time, and data 

are available. Tree such indicators are described in 

this section:

• Crop insurance payments (for losses caused by 

extreme climate conditions)

• Total factor productivity 

• Heat-related mortality of agricultural workers

Many other potential socioeconomic indicators 

were considered during the development of this 

report but were not included because they did not 

meet the report’s primary criteria. Te authors 

nevertheless believe that issues such as wholesale 

and retail price information, proftability of 

agricultural production, land values, adaptation 

investment, deployment and “rate of deployment” 

of adaptation methods, and food storage costs hold 

considerable promise as indicators if matters of 

attribution and public availability of data can be 

resolved.

CHAPTER 6 Climate Indicators for Agriculture

47
One way farmers can manage economic risk is 

by purchasing crop insurance that can help cover 

fnancial losses from reduced production. Te 

Risk Management Agency (RMA) of the USDA 

administers the Federal crop insurance program 

(CRS 2019; USDA RMA, n.d.). Te crop insurance 

policies available through the program are sold and 

serviced by approved insurance providers (AIPs) 

(USDA RMA, n.d.). Te program is designed to 

protect crop producers from the risks associated 

with adverse weather, such as droughts, excess 

precipitation, and foods; weather-related plant 

diseases and insect infestations; and declines in 

commodity prices (CRS 2019). Crop insurance in 

the United States dates back to the 1930s, and RMA 

documents the amount and causes of claims paid 

each year. Tis record of crop insurance payments 

to agricultural producers over time provides a 

quantitative record of costs that refects the efects 

of extreme weather and the climate damage 

experienced by the agricultural sector. 

Importance to U.S. Agriculture

Extreme weather conditions, such as intense rainfall 

and droughts, pose major challenges for agriculture 

in the United States and other nations. A recent 

analysis shows that excess precipitation and drought 

were the largest drivers of loss payouts for the 

United States as a whole from 2001 to 2016 (Reyes 

and Elias 2019). 

Drought and precipitation are monitored in the 

United States by national, State, and regional 

observation networks, many of which keep data 

records that extend back a century or more. NOAA, 

the Environmental Protection Agency (U.S. EPA), 

and USDA all maintain and make available data 

about drought and precipitation conditions. Te 

Palmer Drought Severity Index (PDSI) is one of the 

climate change indicators that is tracked by the U.S. 

EPA (2016a). 

Te records maintained by these organizations 

provide valuable information about the changing 

climate context for agriculture but do not provide 

a full picture of the efects of such change. Te actual 

impact of extreme conditions also depends on the 

adaptive capacity of the afected crop system and 

actions taken by producers in response. Vulnerability 

to drought and excess precipitation varies widely 

across regions and producers because of diferences 

in access to groundwater and diferent levels of 

investment in and deployment of adaptive practices, 

such as irrigation and drainage systems, which can 

reduce impacts (Wallander et al. 2013). 

Tere is strong scientifc agreement that intense 

rainfall and drought are very likely to increase in 

the future, both globally and in the United States 

(USGCRP 2017; IPCC 2013). If such projections 

prove accurate, crop insurance payouts for losses 

from excess precipitation and drought are likely 

to rise, but successful adaption measures could 

minimize or prevent such increases. Comparison 

of historic, current, and future payouts over time, if 

normalized to account for infation, changing prices, 

changes in acreage, and changes in the number 

of producers covered, can provide a quantitative 

measure of the efects of climate change (Hatfeld et 

al. 2018; Reyes and Elias 2019). Te record of crop 

insurance payments also provides information about 

the relative importance over time of diferent weather 

and climate risks faced by producers in diferent 

regions (Reyes and Elias 2019).

Producers can purchase crop insurance to help 

cover costs incurred by extreme climate conditions, 

including both drought and excess rainfall, that 

prevent planting, damage crops, or prevent harvest. 

RMA tracks and categorizes payments made by 

crop insurance by cause of loss, which include hail, 

heat, freeze, cold wet weather, wind/excess wind, 

hot wind, irrigation failure, and food in addition 

to drought and excess precipitation (see fgure 6.1). 

Tese data provide a quantitative record of economic 

damages imposed on producers by extreme 

conditions that includes the ofsetting efects of any 

adaptive measures taken. If properly normalized, 

they can provide information about how damages 

are changing over time. Such information can be 

aggregated at the national level to provide a record 

for the country as a whole but may be most useful 

when organized by region because agricultural 

activities, climate and weather risks, vulnerability, 

and adaptive capacity vary widely across the 

United States. 

Chapter 6 Climate Indicators for Agriculture

48

6.1 Crop Insurance 

Payments
Data Sources and Limitations

Data about payout amounts and causes are available 

through USDA RMA. Te USDA AgRisk Viewer, 

available at the USDA Southwest Climate Hub 

(n.d.), is a tool that enables access to and analysis of 

cause-of-loss data. 

As previously noted, insurance coverage varies by 

region, cropping system, and producer needs. It is 

important to note that producers can also seek relief 

from other programs to cover losses. Terefore, 

crop insurance payouts may not provide a complete 

picture of the economic impact of changes in 

extreme weather conditions (Hatfeld et al. 2018). 

Policy changes and lack of monthly data make it 

problematic to compare data from before and afer 

2001 or analyze trends before 2001 (Reyes and 

Elias 2019). For accurate analysis and comparison 

of trends over time, data must be normalized to 

account for infation, changing prices, and changing 

levels of coverage. Data from single years or events 

may be more representative of natural variability 

than climate change; decadal or longer records 

are much more likely to show meaningful climate 

change efects.

Figure 6-1. Adjusted annual indemnities for the Nation and USDA Climate Hub Midwest region listed by cause of loss. 

Data have been normalized to account for infation, product output, and premium changes. Dashed black lines indicate major policy changes afecting 

crop insurance, including 2008 and 2014 “Farm Bills.” Regional data show that climate and weather risks vary by location. Figure adapted from Reyes 

and Elias (2019).

Chapter 6 Climate Indicators for Agriculture

49

Crop Insurance Payouts for Extreme Climate and Weather Losses – An Example

A recent analysis by scientists at the USDA SW Climate Hub examined RMA cause-of-loss data from 2001 to 

2016 (fgure 6-1; Reyes and Elias 2019). Te analysis shows that drought and excess precipitation accounted 

for about 71 percent of insured losses during this period for the country as a whole. Tese were also the 

two greatest causes of loss in most regions. In the major drought year of 2012, when “at least 81 percent of 

the contiguous United States was under at least abnormally dry conditions” (C2ES 2020), and more than 

75 percent of national normalized indemnities were due to drought, totaling more than $4 billion in the 

Midwest alone. 

In terms of national-scale trends over time, there was not a statistically signifcant increase in the monthly 

or annual payments for drought or excess precipitation from 2001 to 2016. Some monthly records for some 

regions did exhibit such increases. However, monthly data for specifc regions should be interpreted with care 

because the short-term data can be more representative of natural variability than long-term climate change 

and because they may also be afected by specifc localized market conditions and sales patterns that are 

difcult to normalize. 

Drought

Excess Moisture/Precip/Rain

Hail

Heat

Freeze

Cold Wet Weather

Wind/Excess Wind

Failure Irrig Supply

Hot Wind

Flood

Other

Adjusted Annual Indemnities 

(Hundreds of Millions of Dollars) 80

60

40

20

0

200120042007201020132016200120042007201020132016

MW (MN, WI, MI, MO, IL, IN, IA, OH) National
Management costs required to support upward 

crop yield trends and protect animals in a 

changing climate create challenges to maintaining 

productivity advances in U.S. agriculture. Te 

USDA Economic Research Service (ERS) has 

used multifactor productivity measurement in 

the Federal statistical program since 1960 (Ball et 

al. 2016; Wang, Nehring, and Mosheim 2018). It 

is the ratio of aggregate outputs (including crops, 

livestock, and other farm-related outputs) to 

aggregate inputs (including land, labor, capital, and 

intermediate inputs, such as purchased services, 

energy, and fertilizer); see USDA ERS (2019a) for 

data documentation. It is usually measured as an 

index set to 1.00 (or 100%) in a base year. Changes 

in TFP over time represent the percentage change 

in the efciency in which these inputs are converted 

into outputs, relative to the base year. Because of the 

increasing impact of climate change on agriculture, 

the relationship between TFP and climate is critical 

to understanding future U.S. growth in agricultural 

productivity (Liang et al. 2017).

Historically, trend growth in TFP of U.S. agriculture 

has been about 1.46 percent per annum over 

1948–2017, refecting adoption of new technologies 

and improved practices (ERS 2019). However, TFP 

fuctuates around trend growth due to weather 

and other environmental shocks. Long-term 

environmental degradation (to climate, soils, etc.) 

can ofset gains in TFP from technological advances 

and slow or even reverse growth in TFP.

Importance to U.S. Agriculture

TFP provides a comprehensive measure of agricultural 

performance by taking into account not only changes 

in crop and animal yield but also changes in cost 

of production and commodity mixes produced by 

farmers (Fuglie, Wang, and Ball 2012). Trends in crop 

production (total harvested crop volume) as well as 

yield and land productivity (harvested crop volume 

per acre) have long been measures of agricultural 

success. But increases in yield may involve additional 

inputs, such as fertilizer, or adoption of new crop 

management technologies that raise the cost of 

production, though presumably at a rate that allows for 

maintaining or increasing proftability in the absence 

of rising crop prices. Recent changes in climate and 

extreme weather have generated management costs 

for both crop and animal agriculture that would 

presumably decrease proftability in the absence of 

rising prices. Te quantitative relationship between 

TFP and climate at regional scales provides critical 

understanding of whether current U.S. agricultural 

productivity growth will continue into the future. 

From a State-by-State analysis over the period 

1960–2004, Njuki, Bravo-Ureta, and O’Donnell (2018) 

found that weather efects contributed negatively to 

TFP growth across the Pacifc region, the Southwest, 

parts of the Midwest, and the Northeast and positively 

in the northern plains and mountain States. OrtizBobea, Knippenberg, and Chambers (2018) found that 

agriculture in the Midwest is becoming more sensitive 

to climate and thereby more vulnerable to climate 

change. Continuation of such trends would ultimately 

infuence national or international food security.

Chapter 6 Climate Indicators for Agriculture

50

6.2 Total Factor Productivity (TFP)
National Total Factor Productivity – An Example

Wang, Nehring, and Mosheim (2018) described how TFP has changed from 1948 to 2015 (fgure 6-2). Tey 

examined a scenario where technological advances and other adaptations to climate change over the United 

States followed recent historical rates. 

Data Sources and Limitations

Growth in TFP over time is usually interpreted 

as the rate of technological, managerial, and 

systems change. But for sectors like agriculture, 

where production relationships are highly 

sensitive to environmental inputs, changes in 

TFP refect the net efect of many technical and 

environmental changes. With empirical data, it can 

be difcult to disentangle the efects of technical 

and environmental drivers, although modeling 

eforts can be helpful. Agricultural research and 

development are needed to generate climateadaptive technologies that can ofset yield losses 

due to climate change (Liang et al. 2017). Plastina 

and Lence (2018) explored the contribution of 

Year

Total output Total farm input

1948

0.0

0.5

1.0

1.5

1958 1968 1978 1988 1998 2008 2015

2.0

2.5

3.0

Total factor productivity (TFP)

Index (1948=1)

Figure 6-2. Trends in total farm input, total farm output, and TFP (ratio of output to input) from 1948 through 2015. 

Data are expressed with an index that is calculated relative to the data in 1948, which are set to 1. Data source is USDA ERS, Agricultural Productivity in 

the U.S. data series (Wang, Nehring, and Mosheim 2018).

the diferent components of TFP change to U.S. 

agricultural productivity, with the ultimate goal 

of helping policy makers identify efective ways to 

foster productivity growth.

TFP requires data that are not readily accessible, 

such as the way in which climate information 

is factored into the model. Although numerical 

uncertainties are large, the indicator identifes 

the relative importance of diferent regional 

and seasonal climate change factors to TFP. For 

instance, Liang et al. (2017) found that the top three 

contributors to future TFP declines due to climate 

change are (in decreasing order of importance) the 

projected warmer summers in the Midwest, the 

warmer autumns in scattered regions across the 

Southwest, and warmer springs in the Southwest.

Chapter 6 Climate Indicators for Agriculture

51
Heat-related mortality has been identifed as an 

important overall impact of climate change on the 

United States (Melillo, Richmond, and Yohe 2014) 

and is one of the climate change indicators tracked 

by the U.S. EPA (2016c). Te average temperature in 

the United States has warmed by about 1.8°F since 

1895, and there has been a signifcant increase in 

the frequency of heat waves since the 1960s. Both 

trends are projected to continue in the United States 

in coming decades (USGCRP 2017).

Exposure to extreme heat is the leading weatherrelated cause of death in the United States, leading 

to more fatalities than lightning, tornadoes, 

hurricanes, and foods combined (NWS 2019). 

Between 1999 and 2010, extreme heat caused or 

contributed to more than 7,800 deaths in the United 

States; it causes an average of 618 fatalities per year 

(CDC 2012). Most heat-related deaths (86 percent 

–94 percent) occur during June, July, and August, 

and 68 percent–69 percent of the deaths during the 

1999–2010 period were men (CDC 2012). 

Heat stress and heat-related mortality pose a 

particularly serious problem for agricultural 

workers. A recent analysis of overall occupational 

heat-related mortality in the United States from 

2000 to 2010 found that agriculture had a yearly 

rate of 3.06 deaths per 1 million workers, higher 

than any other U.S. economic sector. When 

compared to other economic sectors as a whole, 

agriculture has more than 35 times the risk of heatrelated death (Gubernot, Anderson, and Hunting 

2015). 

Importance to U.S. Agriculture

Climate and weather conditions are strong 

infuences on the agricultural workforce because 

most agriculture occurs outside, with signifcant 

activity in summer months. Although the sector 

has become increasingly mechanized, many 

important activities remain dependent on human 

labor. Te U.S. Bureau of Labor Statistics (BLS) 

defnes agricultural workers as people who maintain 

crops and tend to livestock. Tey perform physical 

labor, such as manually planting, cultivating, and 

harvesting vegetables, fruits, nuts, horticultural 

specialties, and feld crops, and operate machinery 

under the supervision of farmers, ranchers, and 

other agricultural managers (BLS 2019). 

Tracking heat-related workforce mortality over 

time can provide an indicator of climate change 

efects on the agricultural sector and whether such 

efects are increasing. Changes in heat-related 

mortality over time can also provide insight into the 

efectiveness of adaptive measures, such as adequate 

hydration, rest periods, access to shade and air 

conditioning, and monitoring the conditions of 

workers, in reducing the damaging efects of climate 

change on those employed in the agricultural sector.

Chapter 6 Climate Indicators for Agriculture

52

6.3 Heat-Related Mortality of Agricultural Workers
5-year period

1992-1996 1997-2001 2002-2006

0

5

10

15

20

25

30

35

40

45

50

55

60

65

1.2

1.1

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Mortality rate per 100,000 workers

No. of fatalities Mortality rate

†

Number of heat-related fatalities

Figure 6.3 shows the results of a U.S. Centers for 

Disease Control and Prevention (CDC) analysis of 

heat-related crop worker deaths in 5-year periods 

from 1992 to 2006 (CDC 2008). It found that 

fatality rates increased over each period but that the 

increases were not statistically signifcant. 

If heat-related mortality continues to increase as 

temperatures rise, it would show that adaptive 

measures are not being employed widely enough or 

are not sufcient. If the converse occurs, and rising 

temperatures do not result in continued mortality 

increases, it would be evidence of successful 

adaptation to changing climate conditions.

Data Sources and Limitations

Te U.S. Bureau of Labor Statistics (BLS) 

documents the number and causes of workforce 

fatalities in diferent occupations each year, 

including crop worker deaths attributed to heat 

stress. Te data are compiled in the BLS Census of 

Fatal Occupational Injuries (CFOI). Estimates of 

Figure 6-3. Number and rate of heatrelated deaths among crop workers in 

the United States, by fve-year period, 

1992–2006.

Te total number of heat-related deaths 

from 2002 to 2006 was more than double 

the number of heat-related deaths from 

1992 to 1996. Te lef y-axis shows the 

total number of deaths over a time period; 

the right y-axis shows the rate of death per 

100,000 workers. Rates were calculated 

using annual national average estimates 

of employed civilians aged >15 years 

based on the Current Population Survey. 

† indicates the 95% confdence interval for 

fatality rate. (Figure source: CDC 2008.) 

Crop Worker Deaths – An Example

the number of workers employed are derived from 

the U.S. Current Population Survey (CPS). Both are 

produced yearly. 

Mortality rate data for agricultural workers are 

afected by several issues. Changes in methodologies 

for calculating heat-related deaths make it hard 

to compare data from earlier than 1999 with data 

from 1999 or later (U.S. EPA 2016b). Heat stroke 

deaths are not always recognized and listed as such 

on death certifcates, and there are concerns that 

agricultural workforce estimates do not fully capture 

seasonal workers, which can lead to undercounting 

and underreporting (Gubernot, Anderson, and 

Hunting 2015). Finally, the literature on heat-related 

mortality in agriculture is not extensive, with most 

published studies focused on specifc populations 

or locations rather than the entire Nation. Taken 

together, these uncertainties argue for further 

analyses to increase understanding and enable more 

detailed comparison across regions, but they do not 

invalidate the use of heat-related mortality data as 

an indicator.

Chapter 6 Climate Indicators for Agriculture

53
Climate Indicators for Agriculture

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Climate Indicators for Agriculture

70

Authors

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Senior Ecologist 

USDA Ofce of Energy 

and Environmental Policy

Washington, DC

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Associate Director, School of 

Global Environmental Sustainability

Colorado State University

Fort Collins, CO

https://sustainability.colostate.edu

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Director of Strategic Initiatives

University of Nevada, Reno

Reno, NV

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Northeast Regional Climate Center

Cornell University

Ithaca, NY

https://www.engineering.cornell.edu/faculty-directory/arthur-degaetano

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Senior Scientifc Program Manager

Agriculture and Food Systems Institute

Washington, DC 

https://www.linkedin.com/in/rachel-melnick-77a5831a/

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Associate Professor

Purdue University

West Lafayette, IN 

https://www.purdue.edu/fnr/prokopy/

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Professor Emeritus

Iowa State University

Ames, IA

https://www.agron.iastate.edu/people/eugene-takle

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Director, Midwest Regional Climate Hub

USDA Agricultural Research Service

Ames, IA

https://www.climatehubs.usda.gov/hubs/people/

dennis-todey

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Associate Professor

Columbia University

New York, NY

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Production Design and Editorial Coordinator

National Center for Atmospheric Research

Boulder, CO

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Graphic Design and Layout

Colorado State University

Fort Collins, CO

REPORT AUTHORS

AND AFFILIATIONS

APPENDIX B