Iconic Agricultural Crops:
Climate Change Impacts on Peanut, Cotton and Corn in Georgia and Florida
Final Project Report
Submitted to the Bipartisan Policy Center
by
J. W. Jones, K. J. Boote, W-L. Bartels, G. Baigorria, G. Hoogenboom, and K. Hayhoe
July 15, 2012
TABLE OF CONTENTS
Introduction ........................................................................................................................ 1
Objectives ............................................................................................................................ 2
Methods and Procedures ................................................................................................... 3
Background on Proposed Crop Simulation Approach .....................................
Climate in the Southeast USA ..............................................................................
Potential Impacts of Climate Change on Crop Yield .........................................
Engaging Farmers and Extension ........................................................................
3
4
4
5
Results ................................................................................................................................ 7
Climate in the Southeast USA ............................................................................. 7
Potential Impacts of Climate Change on Crop Yield ........................................ 9
Engaging Farmers and Extension ....................................................................... 13
Key Findings ..................................................................................................................... 17
References ......................................................................................................................... 25
Appendix A........................................................................................................................ 30
Iconic Agricultural Crops:
Climate Change Impacts on Peanut, Cotton and Corn in Georgia and Florida
Final Project Report
July 15, 2012
INTRODUCTION
Cropping systems in the southeastern (SE) USA are highly diverse due to favorable weather
conditions with temperatures above freezing most of the year and high, although variable,
rainfall amounts. Although the region is suitable for growing many crops, cotton, peanut, and
corn crops have dominated production and economic activity in Georgia and northern Florida
during the last several decades. Total average annual value of these crops in Georgia and Florida
was more than $1.2 billion in 2008 (NASS, 2009). The average income from cotton was highest
at about $500 million, followed by peanut at about $475 million. We selected these three
important crops in our two states based primarily on acreage and annual value of production.
These three crops were grown on about 2 million acres, with acreage increasing dramatically for
corn during 2007 due partly to increasing demand for biofuel energy. Together, Georgia and
Florida produce about half of the peanut crop in the USA and Georgia is the second highest
cotton producing state in the USA.
There are large variations in yields of these crops over time and space due to annual variations in
weather, particularly rainfall. About 20-40% of the production areas of these crops in the Coastal
Plain are irrigated to increase crop yield and profit and to reduce climatic risks. Effects of
increasing temperatures are less certain as crop response to high temperatures depends on current
temperatures and how they vary during growing seasons. Climate change could cause major
disruptions in production of these crops, thereby affecting a key economic sector with annual
sales in the billions of dollars across the southeast region. This region is subject to seasonal
droughts, seasonally extreme temperatures, hurricane activity, and flooding rainfall events that
have major effects on agricultural production.
Considerable research has been conducted in the SE USA on climate variability and its effects on
crop yields as well as options to reduce risks associated with seasonal to annual variability (e.g.,
Jones et al., 2000; Reilly et al., 2003; Fraisse et al., 2011; Hoogenboom et al., 2008). In addition,
research has been conducted on the effects of temperature and CO2 on several crops grown in
artificial outdoor growth chambers (e.g., Baker, 2004; Allen et al., 2011; Prasad et al, 2003).
However, very little previous research on climate change impacts on these crops has included the
interactive effects of temperature and CO2, particularly under field conditions in this region
(Bannayan et al., 2009; Boote et al., 2010). Research on CO2 effects alone generally agree that
higher atmospheric CO2 concentrations will increase plant biomass and grain or seed
productivity of C3 plants (such as peanut and cotton). For C4 plants, such as corn, increasing
CO2 reduces plant water use rate, but is likely to have little or no yield benefit especially when
nutrients are limiting and/or temperatures are high. For tropical areas, increasing temperatures
generally decrease both biomass and grain productivity. In subtropical zones, such as in much of
Florida and Georgia, temperature effects may be mitigated somewhat by changing planting dates
to avoid growing crops during periods when heat stress or low rainfall is likely. Without adaptive
1
changes, climate change could decrease yields of crops in the SE USA considerably, which
would have secondary impacts on livestock industries that depend on corn for animal feed, on
feedstock for biofuel production, and thus on corn prices. Reductions in production would also
have major economic impacts in the region and the livelihoods of farmers, associated industries,
and the people who live in the region.
Warmer temperatures could reduce yields of peanut, cotton, and corn and shift the optimal
geographic production ranges and/or sowing windows of these crops northward, while more
severe high temperatures will increase the risk of crop failures. Other risks include shifts in late
Spring freezes that could potentially kill early-planted crops. More variable precipitation patterns
will increase the risks of crop damage during heavy downpours and yield loss due to water
shortages as shown by recent droughts. Increasing temperature and lower rainfall are likely to
decrease crop yields and increase demand for irrigation during droughts that occur during the
same time periods when competing demands for water may reduce water available for irrigation.
These changes could have major implications on economic activity as well as land use and water
availability in this region.
Based on our research in the Southeast Climate Consortium ( see www.SEClimate.org and
www.AgroClimate.org), farmers and their advisors are much more interested in weather during
the next few days and on seasonal climate variability than on the longer term changes in climate
(Fraisse et al., 2009; Bartels et al., 2011, 2012). Many of their decisions and plans depend on
weather and anticipated conditions at those few days to several months into the future. We also
learned that farmers are very concerned about regulations that may be imposed to reduce carbon
and other greenhouse gas emissions into the atmosphere. These regulations could affect their
costs of production as well as future availability and cost of energy for irrigation (one important
adaptation strategy). Production costs may increase as water supply becomes less reliable and
more stressed by regional population growth and competing uses. However, increasingly farmers
in this region are concerned about climate change, including how local temperatures and rainfall
will change and at what rate, how these change will affect production of these crops, what
changes should they make, and when should they act.
OBJECTIVES
This research was organized to study climate change impacts on the three major field crops that
are grown in Georgia and North Florida, corn, cotton, and peanuts. Our objectives were:
1. Summarize the most recent climate change projections for the Southeast USA,
2. Estimate the potential impacts of climate change projections on the selected crops (corn,
cotton, and peanuts), and
3. Engage farmers and their advisors in the Southeast to learn from them what type of future
climate information they need and to inform them of our research on these three crops.
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METHODS AND PROCEDURES
Background on Crop Simulation Approach
The above objectives are revised relative to what was originally proposed. We had to change our
approach due to unforeseen technical issues related to downscaling climate change scenarios.
These technical difficulties are summarized here, and in the remainder of the report we present
work that was accomplished.
In the original proposal, our objectives were to use downscaled climate change data for the
Southeast from two of the IPCC AR4 climate change scenarios (A1B and A1F1). These two
scenarios were developed on the assumption of a homogeneous world through globalization in
which there is rapid economic growth, population reaches 9 billion in 2050 and then gradually
declines, and there is a rapid spread of new and efficient technologies (IPCC AR4 Report,
Solomon et al., 2007). The A1B scenario assumed a balanced source of energy over time
whereas A1F assumed a strong emphasis on fossil fuels in the future.
Our proposed approach was dependent on obtaining projections of daily weather data for future
climates in the two scenarios that were downscaled by one of our cooperators (Dr. K. Hayhoe)
using a Modified Statistical Asynchronous Regression Downscaling Method, similar to the
approach used by Hayhoe et al. (2008). We provided historical daily weather data for about 90
sites in Georgia and North Florida where these crops are grown. Hayhoe used these daily
weather data along with the IPCC AR4 projected climate conditions for the Southeast area for
the A1B and A1F1 scenarios to produce daily weather data as projected by each of four climate
models for the two scenarios. Furthermore, that same procedure was used to downscale historical
climate model runs to evaluate how well the procedure worked when used to “project” past
climate conditions. Hayhoe generated 30 years of daily weather data for the historical period as
well as 30 years for each of two scenarios and four climate models, resulting in over 1,000
sequences of daily weather data, each having 30 years. These procedures created a huge set of
past and projected future daily weather data for use in our analysis of how crops would be
affected in the future. The amount of data that we were dealing with was not the main technical
difficulty, although it did contribute to delays in identifying the problem.
We introduced the data produced by Hayhoe into the crop models, but the results were not
realistic and attempts to rectify the problem were not successful. The crop models are very
sensitive to uncertainties in daily weather inputs (Baigorria et al., 2007). The problem was that
the statistical method used to downscale the climate model results produced “outliers” in the
daily weather data files that made them unusable in our crop models. For example, the monthly
mean climate variables at each of the weather station sites in the Southeast seemed to be
reasonable, but daily values of temperature were sometimes less than 0 C in the summertime
(historical time period and future time period), and since any freezing temperatures in the
weather record would kill the crops in our simulated experiments, there were a number of
unrealistic model results. After this problem was identified (after simulating millions of seasons
for the three crops), an attempt was made to refine the downscaling method. The data were again
used in the simulation of millions of crop growing seasons only to find that the problem was not
completely solved. These results were also unusable. Because of these delays and uncertainties
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about when refinements in methods would produce usable results, we used a different method
(the “delta method”, see below) to introduce climate change information into the crop models to
evaluate potential impacts.
Although we had used downscaled climate scenario data before in our crop models, previous
downscaling methods always used historical daily weather data for the past and the “delta”
method (Bader et al., 2008) to create future daily weather data. In the “delta” method, climate
model output is used to determine future change in climate with respect to the model’s presentday climate, typically a difference for temperature and a percentage change for precipitation.
Then, these changes are applied to observed daily historical weather data to produce climates for
input to crop models for impact studies. The delta method assumes that future model biases for
both mean and variability will be the same as those in present-day simulations. One questionable
consequence of this assumption is that the future frequency and magnitude of extreme weather
events are the same relative to the mean climate of the future as they are in present-day climate.
The method that Hayhoe was using is an advanced method and has considerable promise to
improve on the methods we used in the past. However, due to the highly non-linear nature of the
crop models, even very short term weather shocks (freeze or extreme heat) can kill the simulated
crops, even though average climate conditions are near optimal for the crops. More research is
needed to make refinements to the downscaling methods.
Climate in the Southeast USA
We reviewed the most recent reports by the National Climate Assessment for the SE USA to
determine a range of climate change variables (temperature, rainfall, and CO2) to use in our
analyses of climate change impacts on corn, peanut, and cotton crops. This study of recent
climate change findings for the Southeast was used to set ranges of climate change variables for
use in a sensitivity analysis in which potential impacts of these ranges of climate change on
yields and irrigation requirements were simulated for these three crops. These results were also
summarized to help communicate climate change information to farmers and extension faculty in
Georgia and Florida.
Potential impacts of climate change on crop yields
Crop simulation models were used to estimate the potential impacts of changes in climate
(temperature, rainfall, and CO2) for corn, peanut, and cotton in one area in our two states where
these three crops are extensively grown. We used cotton, peanut, and corn crop simulation
models that have been well tested in this region (the DSSAT Cropping System Model; Jones et
al., 2003; Hoogenboom et al. 2004, 2008; Boote et al, 1998). A number of past studies have been
conducted in our states in which these models were used for simulating effects of climate change
and climate variability on soybean, cotton, peanut, and corn (e.g., Jagtap et al., 2002; Irmak et
al., 2006; Reilly et al., 2003; Garcia y Garcia et al., 2006; Guerra et al., 2007; Paz et al., 2007;
Guerra et al., 2008). The newer versions of these models take into account a lower growth and
yield responsiveness to CO2 than earlier versions and new information on temperature
sensitivities analyzed by one of the investigators on this project (K. J. Boote), and presented in
Hatfield et al. (2008) based on publications by Alagarswamy et al. (2006), Boote et al. (2005),
Boote et al. (2010), and Prasad et al. (2003). The analysis used typical soil and management data
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for this location in addition to the 30 years of historical weather data available from the SECC
(www.SEClimate.org). Crop responses to climate change scenarios were simulated in a
sensitivity analysis with temperature increases of up to 7 0C, plus and minus 10% rainfall, and
increases in CO2 up to 600 vpm. The increase in temperature that is projected in the SE USA by
2100 ranges from about 4 to 6 0C (Biasutti et al. 2009; Campbell et al. 2011; Kunkel et al.,
2012). Thus, our analysis includes temperatures increases that range from lower than those
projected to those just above the maximum value to show a range of possible responses to an
uncertain future climate. We also simulated irrigated and rainfed crops and potential adaptation
options of planting date and variety changes.
The specific crop models in this system were the v4.5 versions of the CSM-CERES-Maize corn
model, the CSM-CROPGRO-Peanut model and the CSM-CROPGRO-Cotton model (Jones et
al., 2003; Boote et al., 2005; Boote et al., 2008; Hoogenboom et al., 2008). This was done in two
steps. First, each of these models was evaluated by comparing simulated crop yields to observed
crop yields for a range of climate, soil, and management conditions obtained from field
experiments in the Southeast in recent years. Estimates of parameters for current crop varieties
were made using those available data to provide the most reliable simulations of crop yields
during recent climate conditions and to simulate potential impacts under a range of future
climates. Details of the procedures used to refine and evaluate the models for these analyses are
provided in Appendix A to this report.
Secondly, we used a sensitivity analysis approach to simulate effects of various combinations of
temperature, rainfall, and CO2 changes on yield and irrigation requirements for these three crops.
We performed the analysis for one location where these three crops are grown extensively
(Camilla, Georgia). We used variety characteristics that are typical of the region, particularly
related to growing season length, and simulated both irrigated and rainfed conditions. Historical
weather data for 30 years (1960-1989) for Camilla was used as baseline climate conditions. We
created climate change conditions by modifying each day by specific changes in temperature
(increases of 1, 2, 3, 4, 5, 6 and 7 C), rainfall (plus and minus 10%), and CO2 (current value of
380 vpm and values of 500, 550, and 600 vpm). In addition, we simulated irrigated and nonirrigated production. These combinations were run for 30 years to include effects of annual
weather variability, which created a total of 4,320 simulated growing seasons for each of the
three crops, or a total of 12,960 simulated crop seasons. For the initial simulations, we did not
change planting dates or varieties in attempts to offset the changes in climate in each of the
sensitivity runs. However, we later varied both planting dates and varieties to see whether simple
adjustments in management of these crops might offset some of the negative impacts that were
simulated.
Finally, results were averaged to show responses to each of the climate change variables alone
and in combinations. Graphs were created to show responses to increases in temperature in
combination with changes in rainfall and increases in atmospheric CO2 concentrations.
Engaging farmers and Extension
A major part of our plan was to engage stakeholders to learn from them how they perceive
climate change and climate risks to their production systems and how they might respond to
5
those climate-related threats. SECC researchers have considerable experience in developing
research information and tools for assessing climate risks to agriculture (e.g., Breuer et al., 2006;
Roncoli et al., 2006; Hoogenboom et al., 2007). Engaging agricultural stakeholders can reveal
the kind of information needed by farmers and extension professionals to better prepare for
changes in climate, the costs of such adjustments, and the likelihood that adaptation will occur.
By integrating local and scientific knowledge and perspectives, group interactions can transform
the way research is developed and used.
Presenting and explaining information on climate change in the past and in the future to
stakeholders was considered an important step in gaining their trust and in understanding their
perspectives on this important and controversial subject. Due to the complexity of the underlying
global and regional ocean and atmospheric processes that affect climate in the SE USA, there are
greater differences between climate in our region and climate model results than for other
regions. Climate has changed in different ways in the Southeast than in other regions of the
nation. In particular, temperature has increased less in the SE during the last century than in other
regions of the US (Kunkel et al., 2006; Misra et al., 2011). Understanding these regional
differences is a critical prerequisite to engaging farmers as they are clearly interested in local
climate and managing risks associated with short and longer term climate variability and change.
Within this context, our aim was to be more problem-oriented in order to produce relevant
information that supports decision making. National and regional assessments are identifying
users of climate information and evaluating adaptive capacities among different economic
sectors. Recent studies suggest that by including practitioners in the scientific process to
collaboratively develop questions, research pathways, and methods, we can forge stronger
relationships among researchers and decision makers (Averyt, 2010). Participatory approaches
are used as mechanisms to facilitate discussions for knowledge sharing and learning among these
diverse stakeholder groups (Marx et al. 2007; Collins and Ison, 2009; Weber, 2011; Ingram et
al., 2012).
This project provided the incentive and partial support for long-term engagement of farmers in
southern Georgia and Alabama and in northern Florida who produce corn, peanuts and cotton. A
climate working group (referred to as the Tri-State Row Crop Climate Working Group) was
organized, starting in 2010 and is continuing through the current time. Biannual working group
meetings have been held (and are continuing) in rural areas in southern Georgia and northern
Florida to discuss climate, its impacts on cropping systems, how farmers have dealt with major
climate events in the past, such as drought and flooding, and how they might modify their
cropping systems to be more resilient to climate variability and change in the future. The
working group was initially created to achieve objectives of this project; they have also been
partially supported by other funds available to the SECC (see www.AgroClimate.org) and the
Florida Climate Institute (FCI, see www.FloridaClimateInstitute.org). These workshops were
jointly organized by social scientists and extension specialists in the SECC and FCI and by
county Extension Agents in Georgia, Alabama, and Florida.
This report summarizes what we learned through engaging farmers, extension professionals and
scientists in the tri-state climate working group for row crop agriculture regarding the types of
adaptation strategies that are appropriate for particular farming systems. We summarize findings
6
from five workshops to reveal how stakeholders perceive factors that shape their capacity to
adapt to changes. We also describe processes used to catalyze dialog and learning among
stakeholders. An attached report provides more details about this rich set of experiences (Bartels
et al., 2012) and is submitted as a part of this final report; we left it intact as prepared so that
important details are retained.
RESULTS
Climate in the Southeast
A key characteristic of climate in the SE region is its seasonal and annual variability. This
variability over time is strongly influenced by ocean and atmospheric conditions outside of the
region, such as surface temperatures of the oceans and atmospheric conditions that include the El
Nino-Southern Oscillation (ENSO) phenomenon, the Bermuda High, North Atlantic Oscillation
(NAO), and the Pacific Decadal Oscillation (PDO) (e.g., Kurtzman and Scanlon, 2007; Misra et al.,
2011; Obeysekera et al., 2011). Increased scientific understanding of these global conditions has
led to improved seasonal to annual climate outlooks over much of the SE, such as the ENSO
phase influence on hurricane activity and on winter and spring rainfall and temperature across
Florida and parts of Georgia. Extreme climate events are highly important, such as drought,
tropical storms and hurricanes, severe thunderstorms with tornadoes, and extreme heat and cold
events.
Past Trends in the Southeast Climate. Generally, climate change in the SE has been more subtle
than in other regions of the USA. However, there have been trends, and these may be helpful for
making statements about near-term projections of climate, particularly for informing adaptation
decisions and policies.
The SE is one of the few regions globally that did not have an overall warming trend during the
20th century (DeGaetano and Alen, 2002; Trenberth et al. 2007; Portmann et al. 2009; Misra et
al., 2012). There are differences in trends among weather stations, indicating that temperatures
decreased some at many weather stations but increased at other stations, particularly those in
urban areas and where changes in land use occurred. Past studies have tried to attribute this
cooling trend (sometimes referred to as a “warming hole”) to changes in sea surface temperature
(Robinson et al. 2002), land-atmosphere feedback (Pan et al. 2004), and/or internal dynamics
(i.e., chaotic behavior of the climate system; Kunkel et al. 2006). Portmann et al. (2009) suggest
that these cooling trends relate to the fact that the May-June period in the SE US represents a
period of abundant rainfall, which causes more evaporation and cloudiness that could result in a
cooling trend, thus compensating for local greenhouse warming.
However, since 1970 temperatures in the region have steadily increased; the decade of 20012010 is the warmest on record. Furthermore, there is an increasing trend in temperature
extremes, with higher frequencies of numbers of days with high maximum and minimum
temperatures. Already during the 2012 summer, many cities across the US, including in the
Southeast, have routinely experienced record-high temperatures. There also has been a decline in
7
the number of extreme cold temperatures in the region, although there is considerable decadal
and regional variability in cold temperature events.
Precipitation in this region is cyclic, influenced to a large extent to the global variations in sea
surface and atmospheric conditions. There have been no discernible trends in annual or summer
precipitation during the last 100 years, except along the Gulf Coast where it has increased.
However, there has been an increase in the inter-annual variability of precipitation during the last
few decades, with more wet and also more dry summers relative to the middle of the 20th
century. There has not been any discernible trend in drought.
Near-Term Climate Outlook. Although some research has been reported recently on the
adequacy of CMIP5 climate models to predict near-term (e.g., 10-20 year) changes in climate
(Kim et al., 2012), near-term climate variability prediction skill of models used in past IPCC
assessments have not been demonstrated (van Oldenborgh et al., 2012). However, many
decision and policy makers need to know what adaptation measures need to be implemented now
to avoid unwanted outcomes as changes occur over this near-term time period. Natural climate
variability will continue to play a dominant role during the future, with climate varying
seasonally and annually as it has during the past. However, there is likely to be continuing longterm trends (e.g., of continuing increases in temperature, occurrence of extreme daily and 5-day
precipitation, and number of days with extreme high temperatures) (Kunkel et al., 2012).
Longer-Term Climate Outlook. Temperatures across the SE are expected to increase during this
century (Kunkel et al., 2012). Because of natural climate variability, long term annual average
temperature increases are expected to fluctuate as in the past. Increases of about 2 to 3 C are
projected by mid-century and up to 4 to 6 C by the end of the century depending on the scenario
and area, due to both natural and human influences, with higher increases in the summertime
when most field crops are grown (Kunkel et al., 2012). Projections of future precipitation
changes by climate models are less certain than those for temperature. However, climate model
projections provide a range of precipitation scenarios that should be considered in assessment
and planning of response strategies, ranging from decreases to increases in precipitation.
Averages of model projections suggest that, through the current century, the southern and
western parts of the Southeast will have reductions in precipitation of up to 10%, while the rest
of the region may have increases in annual average precipitation of about 5-6% (Kunkel et al,
2012). Projections suggest that reductions in rain in the summer season will be larger than in the
other seasons. Furthermore, precipitation and temperatures will continue to vary considerably
from year to year due to the influences of ocean and atmospheric processes that occur external to
our region, such as the El Nino-Southern Oscillation (ENSO) phenomenon, the Pacific Decadal
Oscillation (PDO), North Atlantic Oscillation (NAO), and the cycling of the Bermuda High
pressure system (Kunkel et al., 2012).
Annual to decadal temperature variability contributes to the uncertainty in temperature
projections from climate models, particularly during the short term. This suggests that
projections over the short term (before midcentury) are uncertain and should only be interpreted
as indicative of the kind of changes that are likely as the climate warms. The number of days of
extreme high temperatures is projected to increase in both Georgia and Florida. Similarly, the
frequency of warm nights is projected to increase in some areas and minimum temperatures are
8
projected to increase as overall warming occurs, which will increase the duration of freeze-free
seasons. These trends could have important implications on crop yields, and in some areas, more
favorable temperatures may evolve. Generally, there is more uncertainty in projections of
rainfall. If rainfall in the future has the same characteristics as today, increasing temperatures
will result in higher rates of crop water use and may increase irrigation demand.
Potential Impacts of Climate Change on Crop Yields
Adapt Crop Models for Corn, Peanut, and Cotton in the Southeast:
Crop models that we have been developing and working with for over 20 years were used to
estimate the potential impacts of climate change on these three crops. These models are based on
our understanding of crop physiological process responses to climate, soil and management
practices (Jones et al., 2003, Boote et al., 1998; Hoogenboom et al., 2004). The Cropping System
Model in the Decision Support System for Agrotechnology Transfer (DSSAT) includes models
for corn, peanut, cotton, and 23 other crops. They have been used in many previous studies of
climate change impacts on crop production as well as studying ways that farmers can improve
management to increase productivity and reduce risks (Jones et al., 2003). The models are
computerized simulations that consider daily weather data (temperature maximum and
minimum, rainfall, and solar radiation). Using these models, one can simulate the daily
development and growth rates as weather varies from day to day, and predict final yield that
takes into account the full season of weather data, the soil conditions of the field in which the
crop is grown, and management practices. Models for two of the crops, corn and peanut, have
been tested in Florida, Georgia, and in many other states and countries with successful results
(e.g., Mavromatis et al., 2001; Gilbert et al., 2002; Garcia y Garcia et al., 2006; Boote et al.,
2010). The cotton model is new and has only been tested in a few places in the Southeast (Garcia
y Garcia et al., 2010).
One of the characteristics of these models is that they require information about the varieties
being grown, such as the effects of temperature on growing season length. These characteristics
are summarized as cultivar-specific coefficients that must be estimated for each cultivar or
hybrid that is to be analyzed. These coefficients can be estimated by using field experiments or
yield trials in which a minimum set of weather, soil, and crop data are collected. Tools are
available in the DSSAT system to estimate these coefficients. Because the physiological and
physical processes that are represented in the models by weather-dependent equations, the
models can be used to simulate these crops growing in current and future climate scenarios.
Before we used the corn, peanut and cotton models to assess the potential impacts of climate
change on these crops in our region, we obtained data on currently-grown cultivars and hybrids
in Georgia and Florida, and used those data to estimate the cultivar-specific coefficients that
were then used in our simulation analysis. We have reported on this important preparatory step in
our analysis in Appendix A. The main results of that effort were sets of coefficients for each of
our three crops that can simulate current cropping systems. As noted in Appendix A, results for
corn and peanut were good, and we are reasonably confident that the simulated results for these
crops are reliable estimates of how climate change will influence these crops. Results for cotton
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were less reliable, but provide the best estimates that we have today on cotton responses to
climate change. The uncertainty in predicting how crops will respond to projections of high
temperature, increased CO2 levels, and variable rainfall are largely due to a lack of field
experiments in those conditions. It should also be noted that we have a major international effort
underway currently to evaluate different models and improve them relative to their accuracy in
simulating crop responses to climate change (see www.AgMIP.org).
Simulating Impacts of Climate Change on Yields:
There is considerable uncertainty in how climate will change in the future. Sensitivity analysis
allows one to analyze how much crop yield will change for a wide range of potential future
climate conditions. In this study, a sensitivity analysis was conducted to explore the effects of
climate change variables on maize, peanut, and cotton for one location in Camilla, Mitchell
County, Georgia. This was done using a one-at-a-time sensitivity analysis approach in which
historical weather data for 30 years at that location were modified by adjusting daily observed
values with the “delta” change that was imposed, one variable at a time. The three variables were
temperature, carbon dioxide concentration, and rainfall for both irrigated and rainfed conditions.
Changes in temperature were imposed on both daily maximum and daily minimum temperature
observations from the historical record, for every day of the 30 years.
Based on the National Climate Assessment climate change scenarios, temperature increases may
be up to 7 C (but more likely not more than 4 to 6 C, Kunkel et al., 2012), rainfall is projected to
vary between – 10% to +10 %, and CO2 concentrations would increase up to about 600 ppm.
Thus, we selected one degree increments to temperature starting at 0.0 going to +7.0 0C. Rainfall
was varied from 0.0 to -10% to +10% changes on a daily basis, and CO2 concentrations were
allowed to vary from the current level of 380 ppm, to 500, 550, and 600 ppm.
For all three crops, we used a Norfolk Sandy Loam soil, typical of that area, with a 173 cm deep
profile. For maize, we used the early hybrid “cultivar” using the cultivar coefficients in Table
A.1. The maize crop was planted on April 1 for all simulations, using a row spacing of 91 cm
and 6.5 plants per square meter. The model was run using water and N balances, and 180 kg/ha
of N was applied to the crop in 3 split applications. The peanut crop was planted on May 15 in a
row spacing of 91 cm and plant population of 21 plants per square meter. Since peanut is an Nfixing crop, no N was applied. The peanut variety used was Georgia Green; see Table A.2 for the
cultivar coefficients that were used. The cotton cultivar coefficients given in Table A.2 were
used. We planted cotton on May 15 for the sensitivity analyses, and applied 100 kg/ha of N to
the crop using two equal split applications. Row spacing was 91 cm and plant population was 21
plants per square meter.
Corn Sensitivity Results. Crops have different temperature thresholds below which (and above
which) growth and development cease. For example, the base temperature below which corn
vegetative development ceases is 8 0C, and the maximum mean temperature above which grain
yield completely fails is about 35 0C (Hatfield et al., 2008). The optimal temperature range for
corn yield is between 18 and 22 0C (Hatfield et al., 2008). Average season growing temperature
for corn in Camilla, Georgia, was 24.1 0C (Table 1), which is already above the optimal
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temperature for corn grain yield. This implies that any increase in temperature in this region will
result in lower corn yields.
Figure 1 summarizes corn responses to changes in rainfall (+/- 10%), temperature (up to +7 0C),
and CO2 concentrations (up to 600 ppm) for Camilla, Georgia over a 30 year time period (19601999). Results show that temperatures at this site are already at or above the optimum
temperature for corn grain yields because yields declined (by about 6%) with a 1 degree 0C
increase in temperature and kept decreasing through + 7 0C. Yield decreased by about 35% for
irrigated corn with a 7 0C increase in temperature. For non-irrigated corn, this yield decrease was
almost 50% for the same +7 0C increase in temperature. This shows that high temperature is
more detrimental to corn yields when the crop is not irrigated. Temperature and rainfall interact
to determine how crop yields will be affected by climate change. Higher temperatures have
direct nonlinear effects on corn development, photosynthesis, grain formation, and yield. Higher
temperatures generally shorten the growing season, may increase or decrease photosynthesis
depending on the cardinal temperatures for photosynthesis, and may reduce grain formation and
grain growth rates if temperatures are above optimum levels. Higher temperatures also cause
crops to deplete soil water faster, which will cause faster onset of water stress and reduce
photosynthesis and growth sooner, thus further decreasing yield.
The +10% and -10% changes in rainfall had relatively small effects on corn yield compared with
the range of temperature increases (Figure 1). Similarly, increases in CO2 had small effects on
simulated corn yields. Irrigation demand, estimated by the crop models as the amount of water
needed above that provided by rainfall to satisfy crop water needs, increased by about 45% for 6
0
C higher temperatures (Table 1). Figures 1(e) and (f) show two important responses to climate
change. First, the corn yield decrease per 1 0C temperature increase is larger at higher
temperatures, a response also shown by Lobell et al. (2011) using statistical analysis of corn
yields across a wide range of environments. It also shows that the decrease per 1 0C was larger
for non-irrigated vs. irrigated corn. For example, at a temperature increase of 3 0C, non-irrigated
corn yield decreased almost 12% per additional degree increase whereas irrigated corn yield
decreased only about 5.5% per degree C increase. Thus, for the corn growing seasons that
averaged 26.7 0C (computed when daily temperatures increased by 3 0C in this location), an
additional one degree C increase in temperature would decrease yield of non-irrigated corn by
about 12%, which is in contrast to about a 6% decrease in yield if growing season temperature
was 24 0C. For regions with cooler growing season temperatures (for example, around 17 0C),
this response would be different because small increases in temperature above that lower base
temperature could result in increased corn yields.
A second notable response was that increased CO2 concentrations to between 500 and 600 ppm
mostly offset the negative effects of a 1 0C increase in temperature. However, for temperature
increases above 1 C, increases in CO2 were not effective in ameliorating the negative effects of
temperature increases on corn yield.
Tabular results of the sensitivity analysis are presented in Table 1 for corn for temperature
increases up to +6 C, the four CO2 levels, and for irrigated and non-irrigated corn. The table
includes the standard deviations of yields and irrigation requirements, which were computed to
show the effects of weather variability during the 30 years of analyses. Variabilities in yields
11
were high for non-irrigated corn; the standard deviation of yield was about 35% of average
yields whereas for irrigated crops, they were about 10-12 percent. This highlights the value of
irrigation in this region; average irrigated yields were higher than non-irrigated yields and they
were much more stable across time.
Another interesting result was that simulated corn yields were higher for higher CO2 levels by
between about 1.5 to 3.3% for irrigated and from 3.5 to 7.0% for non-irrigated corn, depending
on how much temperature increased. Also, irrigation requirements were not affected very much
by higher CO2 (up to 600 ppm); in contrast the large effects of higher temperature were seen as
large reductions in yield and increases in irrigation requirements over the range of temperature
increases that we studied. Generally, these results reflect the fact that photosynthesis in corn (a
C4 crop) does not increase much under elevated CO2 levels. However, our simulated results may
underestimate the effects of the well documented partial closure of stomata under high CO2,
which reduces the rate of corn crop water use (Allen et al., 2011).
We also conducted an exploratory analysis to determine the potential benefits of changing
hybrids (using the two that were described in this report) and changing planting dates. We found
only small differences relative to the hybrid and planting date reported here (results not shown).
However, a more extensive study is needed to evaluate the wide range of hybrid germplasm that
is available to farmers and to fully explore a wider range of planting dates and other management
practices that might help offset the negative effects reported here. Those studies were beyond the
scope of this study.
Peanut Sensitivity Results. Figure 2 shows simulated sensitivities of peanut to climate change
variables. Similar to corn, peanut pod yield decreased at all temperature increases when CO2
concentration was at 380 ppm, indicating that temperatures are also at or above the optimum for
this crop. Hatfield et al. (2008) reported that the optimum temperature range for peanut pod
yield is from 20 to 26 0C. Average peanut growing season temperature at this location was 26.2
0
C (Table 2), which is higher than that for corn because of the later planting date for peanut.
Overall, simulated yield responses of peanut were very sensitive to increases in temperature. For
non-irrigated peanuts, yields decreased by over 70% for growing season temperatures averaging
over 32 0C (increase of over 6 0C relative to historical temperatures). Furthermore, irrigation
requirements increased by over 80% for the range of temperatures simulated, increasing from
155 mm at current temperatures to 285 mm at current + 6 0C (Table 2).
There was a notable difference between peanut and corn, however, in that the higher CO2 levels
increased peanut yields more than for corn, in both rainfed and irrigated management. Simulated
yields at current temperatures increased from 3,986 kg/ha to 4,593 when CO2 increased from 380
to 600 ppm, an increase of about 600 kg/ha or 15% for non-irrigated crops and about 850 kg/ha
for irrigated conditions. However, the positive benefit of higher CO2 levels decreased rapidly as
temperatures increased to the higher values in the study. This was true for both irrigated and nonirrigated cases. For temperature increases of +6 0C, yield increased by about 250 kg/ha for nonirrigated and about 360 kg/ha for irrigated crops. Higher CO2 levels partially offset the negative
effects of higher temperatures on peanut yields; for example peanut yields at 600 vpm CO2 and
for increases in temperature up to 2 0C were about the same as for current temperatures (26.2 0C)
and current CO2 levels (380 ppm) (Table 2). This effect was stronger in irrigated vs. non12
irrigated peanut than in corn, which is to be expected since peanut has a C-3 photosynthetic
pathway vs. a C-4 pathway in corn.
Irrigation requirements also increased dramatically under higher temperatures, becoming 80% or
more higher when average peanut growing season temperature reached 32.1 0C (6 C higher than
current temperatures) (Table 2). Similar to corn, changes in rainfall of +/- 10% caused relatively
smaller impacts on peanut yields compared with temperature increases that are in the scenarios.
Note, however, that these results do not take into account increases in climate variability or
extremes, simply due to the method used to impose these climate change variables on current
climate.
Cotton Responses. Some results for cotton were similar to peanut; both are C-3 crops, in which
photosynthesis and growth respond more strongly to CO2 levels than in C-4 crops like corn.
Also, because cotton was planted at the same time as peanut, growing season temperatures were
similar to peanuts (26.1 C for current climate conditions, Table 3). Hatfield et al. (2008) reported
that optimum temperatures for cotton yield range between 25 and 26 C. Thus, current growing
season temperatures for cotton are already at the maximum threshold for optimum yield, and any
increases would be expected to decrease yield for this crop also. Our simulated responses do
indeed show this response. However, Figure 3 shows that seed cotton yields did not decline as
rapidly for cotton as for corn and peanut, implying that cotton may be more heat tolerant at
above optimum temperatures.
In addition, cotton response to higher CO2 levels was less than for peanut. When CO2
concentration was increased from 380 to 600 ppm for current climate conditions, seed cotton
yield increased from 3,437 to 3,595 kg/ha, about a 5% increase in contrast to about 15% for
peanut. Model response to CO2 is limited by N deficiency, which is likely what caused the low
cotton model responsiveness to CO2. However, we are not as confident in the cotton model
results in general and in particular to responses to higher temperatures. The cotton model has not
been tested nearly as widely as have both corn and peanut models, so we are more uncertain of
cotton results than those of the other crops.
Engaging Farmers and Extension:
Five workshops were held in southern Georgia and Alabama and northern Florida to engage
farmers and Extension personnel in discussions on climate variability, climate change and
associated risks. These workshops were developed to meet the objectives of this project and they
were funded by both this NCEP project as well as by funds obtained from other sources. During
the workshops, questions addressed included: How do production systems respond to variations
in seasonal climate? How might a La Niña or an El Niño event affect crop yield? In which ways
did growers adapt to seasonal variation and extremes of climate events in the past? How might
future changes in climate affect agricultural production in the southeast USA? Can farmers
prepare for future impacts and manage their farming systems differently? Which adaptive
management strategies and technologies are most appropriate for particular farming systems?
Bartels et al. (2011) wrote a report that describes these various workshops and our findings. This
report is attached in full to ensure that the richness of these interactions between researchers,
13
farmers, and Extension personnel is communicated. Here, we summarize a few of the main
findings and we refer the reader to the attached report for details.
Learning from the past; planning for the future. By engaging with growers on a personal level and
listening to their stories, we were able to better understand how families have adapted their
agricultural systems and practices through time. The growers discussed important events that shaped
southern farming throughout generations, going all the way back to about 1825. Discussions revealed
that early settlers to the southeast relied on turpentine and tung oil production. Later, producers
adopted tobacco, cotton, peanuts, watermelon, and corn for livestock, hogs and chickens. Farm sizes
have also changed in this region; different policies, market opportunities, and technologies have
influenced these changes. Participants discussed the important influence that market prices have on
farmers’ decisions and how pests, such as the boll weevil, impacted crop production. Growers in the
group explained that irrigation expanded rapidly in the southeast in the mid-1970s and 1980s and
changed the agricultural landscape. Before the introduction of center pivot irrigation, farmers had to
move pipes across the fields, which required high inputs of labor. Irrigation emerged as an insurance
strategy against climate variability. However, several farmers mentioned the challenges associated
with a lack of available water (some wells must be dug very deep to reach groundwater), permitting
regulations, and the equipment and energy costs needed to operate these irrigation systems. Some
farmers mentioned that if energy costs continue to increase it would not be profitable to irrigate.
Our main interest was on climate changes in the past, how farmers perceived those changes and
responded, and how they might adapt to a future changing climate. Growers recalled good and bad
years that they related to extremely dry, wet or hot conditions. They noted that although the average
rainfall for particular years might appear normal in historical records, the distribution of that rain can
dramatically affect yields. Therefore analyzing precipitation averages might not be particularly
useful. Similarly, participants observed that spatial variation of rainfall can result in differing yields
from farm to farm and county to county. Furthermore, the combination of conditions, such as heat –
in addition to dry spells – can compound risks. In these situations plants become stressed and are
more susceptible to increases in damage from diseases such as aflatoxin. The period of time (of year
and of day) during which crops are exposed to extreme heat also has dramatic effects on yield (e.g.
heat can kill peanut pollen in late June and July, and high night temperatures can impact the setting
of peanut fruit).
Opportunities and constraints to adaptation: Three of our workshops focused on understanding the
available adaptation options that can reduce climate-related risks. Participants identified specific
opportunities and constraints that might be associated with adapting their cotton, peanut and corn
production systems. Growers can sometimes adapt farm-level management decisions such as
planting or harvesting dates, crop variety choices, or pest and disease control schedules. However,
they perceive that they have limited control over factors such as markets, farm and energy policies,
and local infrastructure (irrigation systems or processing plants for cotton and peanuts). A summary
of the perceived opportunities to adapt management systems are summarized below.
1. Plant alternative crops - Shift rotation
2. Adjust planting date
3. Consider different varieties
4. Modify spatial distribution of plants
5. Fine-tune spray schedule of herbicide and fertilizer
6. Adapt tillage
14
7. Improve maintenance of irrigation equipment and increase water harvesting in ponds
8. Prepare better for harvesting
Constraints identified could be categorized into three levels: Macro-level factors, such as policies and
markets; meso-level challenges, such as constraints within the commodity industry or farming
system; and finally, those aspects at the micro-level that affect the household and individual decision
making as indicated in Figure 4.
Figure 4. Perceived constraints to adaptation (from Bartels et al., 2012).
There were two key macro-level factors: markets and the farm-bill. Growers plant particular crops
when market demand is high. These price fluctuations drive short term, year-to-year decisions far
more than climatic factors. “It‘s supply and demand. You know, if cotton prices go to a dollar a
pound, we‘re going to figure out a way to grow cotton” (Grower comment). Even for long term
planning, farmers and extension professionals perceived that the farm-bill would be the major driver
of opportunities and decision making at the farm-level. “As far as what crops were going to be
grown 20 years from now, the farm bill probably impacts what farmers are going to grow more than
anything” (Grower comment).
Meso-level factors tend to dictate what crops are planted and are mainly associated with the
supporting industry for particular crops and the suite of equipment and infrastructure that farmers
have. Farmers are limited in their ability to change their farming systems if they have invested in
large planting or harvesting machines. In addition, storage or processing facilities present
infrastructural challenges to changing crops. The commodity industry itself essentially drives the
kinds of choices available to farmers. “Well, the peanut industry in the southeast is not going to let
there be a dramatic drop (in peanut planting). They are going to maintain certain acreage of peanuts
because they can‘t do anything in these peanut shellers except shell peanuts. They can‘t do anything
in the cotton gin except gin cotton” (Extension professional comment).
Irrigation systems are often perceived as insurance against climate extremes. Some growers and
extension professionals reported the need for government policies that would support incentives to
reduce the costs associated with initiating and maintaining irrigation systems. However, high
15
installation and maintenance costs are not the only limitations to a growers’ ability to irrigate their
crops. Other aspects, such as the availability of groundwater, permitting structures for pumping, and
land tenure systems are also important constraints.
Household adaptation options involve evaluating and comparing management strategies and
technologies. Since investments in agricultural infrastructure often constrain growers from rapidly
changing their production systems, management approaches could perhaps offer smaller, gradual
adaptations that make farming systems more efficient, cost effective, and less vulnerable within a
changing and variable climate. Practices such as strip tillage and precision irrigation, for example,
can reduce costs, maintain productivity, conserve water, and be more resilient to climate variability
and climate change.
During the fifth workshop, we discussed the perceived usefulness of climate forecasts and future
projections in terms of how these may influence their decision making. Participants indicated that
weather or seasonal forecasts were more useful to them for their work than longer-term projections
(Figure 5). This was not surprising in that farmers have many decisions to make during growing
seasons, many of which are sensitive to weather conditions. Additionally, farmers are constantly
adapting to weather conditions during each season and are increasingly interested in knowing more
about climate during the next season as they plan their specific production activities and decide on
purchasing seeds, fertilizers, and other inputs and on their own management calendars. Most of the
participants (about 60%) did not think that long term climate change projections would be of direct
benefit to them, except perhaps as those projections may affect the farm bill, other policies, and
markets.
Figure 5. Perceived usefulness of forecasts and projections at specific timescales among row crop
stakeholders (N=57) in the southeast USA (Bartels, et al., 2012)
16
KEY FINDINGS
Current season growing temperature averages in this region are already at the upper range of
temperatures that support optimum yield of corn, peanut, and cotton. An increase of just one
degree C on average during the current growing seasons of these crops are likely to result in
reduced yields. For example, decreases in yield for corn, peanut, and cotton were 6.7%, 10.5%,
and 3.5%, respectively, when temperatures were one degree C higher under non-irrigated
conditions and 4.3%, 7.6%, and 2.1% under irrigated conditions. Also, with a one degree C
increase over current temperature, irrigation requirements are likely to increase by 7.1% for corn.
Because weather varies from year to year, it is likely that we can identify a year from the past
that has a one degree higher temperature than the average that we computed over 30 years. For
small changes in temperature and rainfall, it would be useful to identify past years of weather
that have these small changes that are likely to occur over the next decade or so, and to better
identify management options that are more resilient to those conditions. Although our study did
not explore a wide range of alternative management practices, this could be done in a future
study. Researchers in the SECC are currently analyzing past data to find historical analogs for
such purposes.
However, temperature increases that are projected for the longer term are more detrimental to all
of the crops, and a historical analog approach for those changes may not be useful. Intermediate
increases in temperature that are likely to occur by mid to end of this century (3-4 0C increases)
resulted in yield decreases of about 30% for corn. There may be ways for farmers to adapt to
these changes, such as improved heat and drought tolerant varieties, improved irrigation
efficiencies, and shifting the time of the growing season to best take advantage of the
temperature and rainfall patterns that evolve.
Irrigated crops were affected less than those grown without irrigation, demonstrating that there
are interactive effects of rainfall and temperature on yields. However, higher temperatures also
increased irrigation requirements considerably. A major question would be whether water
supplies will be adequate in the future to support such increases in pumping water for irrigation.
Energy costs may also increase, raising important questions about the likelihood of further
expanding irrigation to help farmers adapt to climate change.
Our results indicated that the projected increase in CO2 concentration will be helpful for peanut
and cotton. An increase of CO2 to 600 ppm is likely to offset yield losses due to a +1 degree C
temperature increase. For peanut, our results indicated that higher CO2 may compensate for up to
a +2 C increase in temperature. Under higher temperatures, elevated CO2 levels were less
effective; yield response to higher CO2 levels decreased as temperature increased beyond 2 0C
above current conditions.
Farmers and Extension personnel in our states are generally aware of the impacts of drought on
crop yields, and are also aware that higher temperature reduces yields. They also were aware of
potential adaptation options under climate change conditions. However, an important finding is
that farmers and Extension agents who advise them have not been strongly interested in climate
change. They are highly interested in weather that may occur during the next week as they have
17
many decisions to make during a growing season that depend on weather (such as irrigation,
pesticide applications, harvest preparation, etc.). They are also interested in climate predictions
for the next growing season, as they may change decisions as they plan for what to plant, money
to borrow, and what inputs to purchase. These results highlight the fact that most decisions that
farmers make occur over time horizon of weeks to months to up to a few years, not the 50-100
years that are usually emphasized in climate change analyses.
Perhaps more importantly, farmers identified the Farm Bill as a really important determinant of
their decisions every year. They are looking for opportunities that are written into the Farm Bill
and are very concerned about any regulations that may occur due to the climate change debate,
such as carbon taxes. Farmers are highly adaptive, and they continue to be keenly interested in
climate impacts on their production systems and what they might do to be more resilient and
reduce economic risks. However, the Farm Bill and markets have the most influence on how
they change their overall agricultural systems.
This research has led to our improved confidence in the usefulness of the crop models for
simulating combinations of CO2, temperature, and precipitation. The simulated sensitivities to
these variables are reasonable relative to prior literature. They show relatively smooth trends
across the wide range of temperatures and CO2 levels tested. However, there is a need for
additional experimental data to test the model responses over this wide range of higher
temperatures and CO2 levels. The differential simulation outcomes for cotton and peanut need to
be further evaluated because current scientific literature indicates cotton is more sensitive to
elevated temperature than peanut (Reddy et al., 2000; Prasad et al., 2003). Thus, the current
cotton model may be too insensitive to temperature, and/or the peanut model may be too
sensitive. Published data on maize sensitivity to temperature is sparse and more research is
needed to parameterize the maize model for elevated temperature. Such an effort is beginning
now, involving researchers from a number of countries (see www.AgMIP.org).
Finally, one of the main overall findings in this study is that corn, peanut, and cotton crops are
highly vulnerable to the increases in temperature that are being projected to occur in the SE USA
during the next century. Because of annual variability in climate, farmers routinely experience
droughts, cooler and warmer growing seasons, and wet conditions that they have to cope with.
But, they have not had to cope with the very high temperatures that are being projected in
particular. Farmers have considerable experience in how variability in rainfall within a season
and from year to year affects crops; many farmers have adapted to this variability by installing
irrigation systems to cope with drought. However, increases of from 4 to 6 0C in the SE could
have major negative effects on crop yield and on irrigation requirements, as shown in this study.
These higher temperatures directly affect the crops’ abilities to produce grain yield and also
magnify the effects of drought periods. Higher temperatures increase water use, causing
increased demand for water for irrigated crops and more severe drought and low yields for nonirrigated crops. Precipitation is also critically important for high yields in this region, but
projected changes in precipitation are more uncertain than and not as extreme as temperature
projections. Our study also demonstrated small beneficial effects of CO2 through interactions
with crop photosynthesis and evapotranspiration processes, but these benefits were minor.
Adapting to the projected changes will be challenging, but extremely important to the future food
and economic security of the region.
18
.
Table 1. Simulated effects of higher temperatures and increased atmospheric carbon dioxide
concentrations on corn yield for non-irrigated and irrigated fields and irrigation requirements for Camilla,
Georgia. Numbers in parentheses are standard deviations computed using 30 years of weather records.
Temperature
Scenario
Season Mean
Temperature (C)
Non-Irrigated
Corn Yield
(kg/ha)
8,201 (2920)
7,649 (2819)
7,178 (2831)
6,410 (2769)
5,794 (2675)
5,058 (2457)
4,450 (2264)
Irrigated Corn
Yield (kg/ha)
24.1
25.0
25.8
26.7
27.6
28.9
29.4
CO2 Level
(ppm)
Current=380
380
380
380
380
380
380
380
11,837 (1319)
11,331 (891)
10,911 (1004)
10,321 (986)
9,762 (1035)
9,078 (1135)
8,295 (1157)
Irrigation
Requirement
(mm)
182 (75)
195 (79)
210 (79)
221 (84)
236 (83)
248 (85)
265 (86)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
24.1
25.0
25.8
26.7
27.6
28.9
29.4
500
500
500
500
500
500
500
8,275 (2945)
7,886 (2877)
7,536 (2915)
6,720 (2834)
6,062 (2801)
5,332 (2593)
4,655 (2363)
11,910 (1146)
11,609 (915)
11,179 (1034)
10,574 (1014)
10,001 (1064)
9,299 (1166)
8,495 (1189)
184 (79)
186 (75)
202 (82)
217 (84)
232 (82)
246 (82)
261 (85)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
24.1
25.0
25.8
26.7
27.6
28.9
29.4
550
550
550
550
550
550
550
8,393 (2957)
8,088 (2895)
7,666 (2953)
6,813 (2857)
6,152 (2825)
5,411 (2624)
4,721 (2394)
11,976 (1153)
11,673 (920)
11,241 (1040)
10,633 (1020)
10,056 (1070)
9,350 (1173)
8,541 (1196)
183 (79)
182 (77)
200 (82)
214 (82)
229 (81)
243 (80)
259 (83)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
24.1
25.0
25.8
26.7
27.6
28.9
29.4
600
600
600
600
600
600
600
8,486 (2960)
8,193 (2898)
7,756 (2968)
6,896 (2872)
6,232 (2847)
5,469 (2656)
4,772 (2417)
12,020 (1158)
11,716 (925)
11,283 (1045)
10,672 (1024)
10,093 (1075)
9,385 (1178)
8,572 (1201)
179 (80)
179 (77)
197 (82)
212 (82)
226 (79)
240 (80)
257 (83)
19
.
Table 2. Simulated effects of higher temperatures and increased atmospheric CO2 concentrations on
peanut yield for non-irrigated and irrigated fields and irrigation requirements for Camilla, Georgia.
Numbers in parentheses are standard deviations computed using 30 years of weather records.
Temperature
Season Mean
CO2 Level
Non-Irrigated
Irrigated
Irrigation
Scenario
Temperature (C) (ppm)
Peanut Yield
Peanut Yield
Requirement
Current=380 (kg/ha)
(kg/ha)
(mm)
Current
26.2
380
3,986 (1134)
5,139 (290)
155 (50)
Current +1
27.2
380
3,565 (1162)
4,746 (420)
163 (60)
Current +2
28.2
380
3,054 (1126)
4,189 (534)
178 (63)
Current +3
29.2
380
2,493 (1038)
3,540 (614)
196 (69)
Current +4
30.2
380
1,914 (894)
2,840 (609)
221 (79)
Current +5
31.1
380
1,362 (687)
2,154 (516)
253 (80)
Current +6
32.1
380
904 (479)
1,549 (360)
285 (91)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.2
27.2
28.2
29.2
30.2
31.1
32.1
500
500
500
500
500
500
500
4,251 (1316)
4,112 (1311)
3,536 (1278)
2,900 (1186)
2,235 (1034)
1,592 (790)
1,061 (548)
5593 (454)
5395 (481)
4765 (616)
4024 (700)
3232 (702)
2457 (584)
1771 (404)
165 (59)
164 (58)
177 (63)
194 (71)
221 (80)
248 (84)
282 (89)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.2
27.2
28.2
29.2
30.2
31.1
32.1
550
550
550
550
550
550
550
4,433 (1362)
4,289 (1353)
3,693 (1326)
3,033 (1232)
2,345 (1076)
1,667 (823)
1,115 (575)
5801 (473)
5594 (501)
4945 (639)
4176 (730)
3356 (727)
2552 (603)
1842 (419)
166 (57)
163 (56)
177 (64)
194 (73)
221 (79)
248 (84)
282 (88)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.2
27.2
28.2
29.2
30.2
31.1
32.1
600
600
600
600
600
600
600
4,593 (1394)
4,443 (1390)
3,830 (1365)
3,150 (1272)
2,438 (1112)
1,737 (853)
1,162 (595)
5980 (489)
5767 (516)
5098 (660)
4309 (756)
3464 (754)
2632 (622)
1902 (431)
167 (56)
162 (55)
176 (61)
195 (73)
220 (77)
245 (84)
281 (90)
20
Table 3. Simulated effects of higher temperatures and increased atmospheric carbon dioxide
concentrations for cotton yield in non-irrigated and irrigated fields and irrigation requirements for Camilla,
Georgia. Numbers in parentheses are standard deviations computed using 30 years of weather records.
Temperature Season Mean
CO2 Level
Non-Irrigated Irrigated
Irrigation
Scenario
Temperature (C) (ppm)
Cotton Yield
Cotton Yield
Requirement
Current=380 (kg/ha)
(kg/ha)
(mm)
Current
26.1
380
3,437 (404)
3737 (163)
143 (49)
Current +1
27.3
380
3,317 (459)
3657 (178)
147 (49)
Current +2
28.3
380
3,115 (531)
3569 (166)
153 (58)
Current +3
29.3
380
2,854 (638)
3436 (176)
167 (61)
Current +4
30.4
380
2,538 (746)
3263 (197)
185 (71)
Current +5
31.4
380
2,132 (802)
2949 (302)
200 (73)
Current +6
32.4
380
1,640 (792)
2448 (457)
223 (78)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.1
27.3
28.3
29.3
30.4
31.4
32.4
500
500
500
500
500
500
500
3,538 (443)
3,517 (436)
3,366 (491)
3,142 (596)
2,852 (706)
2,472 (819)
1,963 (833)
3854 (190)
3802 (176)
3771 (163)
3683 (168)
3540 (211)
3268 (323)
2782 (463)
143 (49)
155 (51)
160 (55)
174 (61)
190 (69)
208 (72)
232 (78)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.1
27.3
28.3
29.3
30.4
31.4
32.4
550
550
550
550
550
550
550
3857 (178)
3853 (176)
3792 (167)
3738 (169)
3606 (212)
3331 (340)
2847 (467)
143 (49)
155 (52)
158 (58)
175 (60)
190 (70)
208 (73)
234 (79)
Current
Current +1
Current +2
Current +3
Current +4
Current +5
Current +6
26.1
27.3
28.3
29.3
30.4
31.4
32.4
600
600
600
600
600
600
600
3,567 (422)
3,544 (415)
3,422 (475)
3,208 (575)
2,936 (707)
2,548 (810)
2,050 (839)
,,
3,595 (408)
3,592 (426)
3,468 (464)
3,254 (558)
2,990 (691)
2,618 (804)
2,115 (834)
3874 (183)
3855 (182)
3822 (177)
3751 (153)
3639 (219)
3386 (338)
2897 (471)
143 (49)
156 (55)
160 (58)
177 (58)
190 (69)
208 (73)
236 (79)
21
(a)
(b)
(c)
(d)
(e)
(f)
Figure 1. Simulated sensitivities of corn to temperature, rainfall, and CO 2 changes in Camilla, GA, averaged over 30
years. (a) Corn yield response to up to 7 C increase in daily temperatures and +/- 10% rainfall. (b) Corn yield
response to temperature increases under different elevated CO2 concentrations up to 600 ppm. (c) Corn irrigation
demand under temperature increases and CO2 levels. (d) Irrigated corn yield responses to increases in temperature.
(e) Fraction change in rainfed corn yield per 1 C increase in temperature as affected by temperature and CO2 level.
(f) Fraction change in irrigated corn yield per 1 C increase in temperature as affected by temperature and CO 2 level.
22
(a)
(b)
(c)
Figure 2. Simulated effects of temperature increases, higher CO2 concentrations, and changes in rainfall
on peanut pod yields in Camilla, Georgia for a 30 year time period (1960 – 1999). (a) Sensitivity of
rainfed peanut yield to increases in daily temperatures under different CO2 levels. (b) Sensitivity of
irrigated peanut yield to increases in daily temperatures under different CO2 levels. (c) Sensitivities of
peanut yield to increases in temperature combined with +/- 10% change in precipitation.
23
(a)
(b)
(c)
Figure 3. Simulated effects of temperature increases, higher CO2 concentrations, and changes in rainfall
on seed cotton yields in Camilla, Georgia for a 30 year time period (1960 – 1999). (a) Sensitivity of
rainfed seed cotton yield to increases in daily temperatures under different CO2 levels. (b) Sensitivity of
irrigated seed cotton yield to increases in daily temperatures under different CO2 levels. (c) Sensitivities
of seed cotton yield to increases in temperature combined with +/- 10% change in precipitation.
24
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29
APPENDIX A
Parameterizing and Evaluating the Crop Models for Georgia and Florida
K. J. Boote, J. W. Jones, and G. Baigorria
Adapt Crop Models for Corn, Peanut, and Cotton in the Southeast.
The crop models were adapted and evaluated for simulating crop yield responses to climate
change in the Southeast. First, the DSSAT maize, peanut, and cotton models were calibrated
using variety trial data from Georgia. For maize, there were 12 years of data available (1997 to
2008), with yields for rainfed and irrigated trials on both short-season maize, and mid-season
maize hybrids at Tifton, Georgia. For peanut, there were 12 years of rainfed and irrigated trials
(1997 to 2008) at Tifton, Georgia, with pod yield taken from two cultivars, mid-maturity Georgia
Green, and a long cycle cultivar either GA-02C or GA-01R. Pod yields for that specific peanut
cultivar (or similar substitute) was selected in each season. For cotton, there were 12 years (1997
to 2008) of lint yields for rainfed and irrigated cotton at Tifton, Georgia (although yields for
1997 were missing for rainfed and missing for 1999, 2000, and 2001 for irrigated crops).
Data included sowing date, harvest date, and harvested yield of maize grain, seed cotton, and
peanut pod yield. The row spacing for maize was 76-cm rows, but the sowing density varied
over time and differed for short-season rainfed (6.5 seed m-2), mid-season rainfed (6.0 seed m-2),
short-season irrigated (7.3 seed m-2), and mid-season irrigated (6.0 seed m-2). For peanut, the
row spacing was 91 cm and the sowing density was 20.8 seed m-2. Row spacing and sowing
density was lacking for cotton, so a 91-cm row spacing and 20.8 plants m-2 was assumed. The
amount of N applied was available for maize and cotton as pre-plant and side-dress amounts, but
dates of application were not known. The N fertilization rate for maize averaged 194 kg N ha-1
for rainfed and 314 kg N ha-1 for irrigated, with approximately one-third of the N applied at
sowing and two-thirds at 46 days after sowing. The N fertilization rate for cotton averaged 99 kg
N ha-1, with half applied at sowing and half at 46 days after sowing. There were no data on
irrigation dates or amounts, so we assumed a conservative automatic irrigation strategy for all
three crops (irrigating when soil water in the top 50 cm fell to 35% of field capacity, and only refilling to 85% of field capacity). This is a conservative application of water, typical of producers
as well as managers of variety trials.
Tifton loamy sand with maximum rooting depth of 180 cm was used in the calibration. The
Tifton loamy sand soil had 13% available soil water. The relative fertility level was assumed to
be the same for all crops and for both irrigated and rainfed management.
The initial conditions for model simulation required non-zero values for initial nitrate and
ammonium. We assumed values of crop residue to be 1000 kg/ha roots and 2000 kg/ha of
soybean crop residue remaining from the previous crop at planting time. The initial nitrate and
ammonium conditions were taken from the mean of six treatments for a prior maize experiment.
These initial conditions of 26.4 kg nitrate N ha-1 and 26.3 kg ammonium N ha-1 were used for
every simulation of maize, cotton, and peanut.
30
Maize Results: The two varieties (Table A.1) ended up being fairly similar; the life-cycle and
the yield potential were not very different. Observed maize yield ranged from 4,440 to 12,945
kg/ha, whereas the simulated maize yield ranged from 2,826 to 12,602 kg/ha. There was
reasonably good predictability of maize yield over the 12 seasons for both irrigated and rainfed
trials, with a root mean squared error (RMSE) of 1,896 kg/ha and a degree of agreement between
model predicted and observed yield (called a d-statistic) of 0.833 (Figure A.1). There was good
predictability of maize season length over the 12 seasons, with a RMSE of 6.4 days and dstatistic of 0.800. The fact that growing season length varied from about 120 to over 150 days
over the different years in the trial data indicates that temperature varied a good bit during these
12 seasons. The mid-cycle varieties yielded an average of 9,478 kg/ha, with a range from 5,750
to 13,488 kg/ha. Simulated yield of short-cycle variety averaged 9,576 kg/ha, ranging from
2,828 to 12,580 kg/ha. Simulated time to physiological maturity averaged 129 days, compared
to 131 days observed. The simulated mid-cycle variety was only 2 days later maturing than the
short-cycle variety, mostly from having a later time to anthesis. The predictability of the midcycle variety was similar to the short-cycle variety, with a RMSE of 1,846 kg/ha and d-statistic
of 0.830.
Table A.1. Two maize varieties calibrated from Georgia Variety Trials, 1997 to 2008. The P1,
P2, P5, G2, G3, and PHINT are variety-specific traits used by the corn model to simulate
different varieties or hybrids of corn, particularly duration of crop growth and grain yield
potential.
Variety
P1
P2
P5
G2
G3
PHINT
GDD1
GDD
# per plt
mg/gr/d
GDD
Short-Cycle
285
0.300
990
785
8.10
39.00
Mid-Cycle
300
0.300
990
795
8.10
39.00
1
GDD is growing degree days
Peanut Results: The two peanut varieties were real cultivars that are dominant in the
southeastern USA. Georgia Green once was the most dominant cultivar and was represented in
all years of the trials. The other cultivar, Georgia 02C, has now become the dominant cultivar in
the region because it is resistant to the tomato-spotted wilt virus. The observed yield over
irrigated and rainfed Georgia Green averaged 4,645 kg/ha, while the simulated mean yield was
5,011 kg/ha. The degree of predictability (see Figure A.2) was slightly less than that for maize,
with RMSE of 1,057 kg/ha and a lower d-statistic of 0.724. The Georgia 02C cultivar yielded
4,659 kg/ha on average (about the same as Georgia Green), and the simulated Georgia 02C
cultivar averaged 5,186 kg/ha. The RMSE was 1,316 kg/ha with a d-statistic of 0.555. Life
cycle was not highly predictable, in part because variety trial managers delayed harvest of
rainfed peanuts in drought seasons by as much as 20 to 30 days. On average, the simulated life
cycle of Georgia Green was 136 days compared to 144 days for Georgia 02C, which is known to
be a later-maturing cultivar. The Georgia 02C cultivar coefficients in Table A.2, compared to
Georgia Green, reflect slightly later flowering, longer time from beginning seed to maturity, and
lower partitioning that characterize later maturity disease-resistant peanut cultivars.
31
Figure A.1. Comparison of simulated and observed maize yields for two hybrids in Tifton,
Georgia, based on use of 1997-2008 maize yield trial data.
Table A.2. Cultivar coefficients for two peanut cultivars and one cotton cultivar, calibrated to
Georgia Variety Trials. The top row of characters are the variables that provide the capability of the
peanut model to simulate different variety growing season lengths, growth potential, and yield.
CUL
EMFL
FLSH
GAGR
GA02C
21.2
Physiological Days
9.2
18.8 74.3
FLSD
SDPM
22.9
9.2
18.8
Cott
on
38.0
11.
19.0
FLLF
LFMX
SLAVR
SIZELF
XFRT
WTPSD
SFDUR
PODUR
THRSH
85.
1.20
cm2/g
270
cm2
18.0
0.87
g
0.69
PD
38.
PD
29
%
80
80.2
89.
1.30
265
17.0
0.83
0.66
39.
30
79
44.0
65.
1.10
170.
300.
0.75
0.18
35.
15
70
32
Irrigated
4000
2000
Georgia Green Peanut, Tifton, GA
Simulated Days to Harvest
Simulated Pod Yield, kg/ha
Rainfed
6000
0
Georgia Green Peanut, Tifton, GA
150
Rainfed
145
Irrigated
140
135
130
0
2000
4000
6000
130
Rainfed
6000
Irrigated
4000
2000
GA-02C Peanut, Tifton, GA
0
135
140
145
150
Measured Days to Harvest
Simulated Days to Harvest
Simulated Pod Yield, kg/ha
Measured Pod Yield, kg/ha
170
GA-02C Peanut, Tifton, GA
Rainfed
160
Irrigated
150
140
130
120
0
2000
4000
6000
Measured Pod Yield, kg/ha
120
130
140
150
160
170
Measured Days to Harvest
Figure A.2. Comparison of simulated and observed peanut pod yields for two cultivars in Tifton,
Georgia, based on use of 1997-2008 peanut yield trial data.
Cotton Results. Cotton is a more difficult crop to simulate for several reasons: 1) the cotton
model in DSSAT is less well-tested, 2) the initial nitrate and ammonium for the soil make a very
large difference in cotton, and 3) phenology data were uncertain because the crop is
indeterminate and is defoliated prior to harvest. The genetic coefficients for the cotton cultivar
in Table A.2 were estimated to give the right life cycle and yield potential. The reported seed
cotton yield averaged 3,476 kg/ha for the rainfed and irrigated crops over the 12 seasons. Model
simulations averaged 3,857 kg/ha, with a RMSE of 928 kg/ha and a d-statistic of 0.485.
Predictability was not particularly good for either yield or life cycle (to harvest date, Figure A.3).
The model generally did not adequately predict the observed lower rainfed yields in the drought
seasons. Causes for insufficient prediction of drought effects are unknown.
33
Simulated Days to Harvest (-10 d)
Simulated Seed Cotton, kg/ha
5000
Rainfed
4000
Irrigated
3000
2000
Cotton, Tifton, GA
1000
0
Rainfed
155
Cotton, Tifton, GA
Irrigated
145
135
125
0
1000
2000
3000
4000
Measured Seed Cotton, kg/ha
5000
125
135
145
155
Measured Days to Harvest (-10 days)
Figure A.3. Comparison of simulated and observed seed cotton yields for one cultivar in Tifton,
Georgia, based on use of 1997-2008 cotton yield trial data.
34