usefulness and uses of climate forecasts for agricultural extension in south carolina, usa
TRANSCRIPT
ORIGINAL ARTICLE
Usefulness and uses of climate forecasts for agricultural extensionin South Carolina, USA
Scott R. Templeton • M. Shane Perkins •
Heather Dinon Aldridge • William C. Bridges Jr. •
Bridget Robinson Lassiter
Received: 1 September 2012 / Accepted: 30 July 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract Farmers and extensionists can use forecasts
about agro-climatic conditions to reduce risks of agricul-
tural production. Eighteen extension agents, researchers,
consultants, and farmers provided feedback about decision
support tools that utilize such forecasts during focus groups
that were conducted in Florence, South Carolina on Janu-
ary 14, 2011. Climate Risk and County Yield Database
were the tools most selected as potentially useful for
agricultural extension in South Carolina. An irrigation
scheduler was the most frequently mentioned tool to be
developed. Also, a survey of Clemson University’s exten-
sion personnel was conducted in January and February
2011 to assess interest of South Carolina’s growers and
producers in using climate forecasts, eleven potential uses
of climate forecasts by extension’s clientele, and potential
usefulness to extensionists of twelve specific forecasts.
Clemson’s extensionists represent approximately 97 % of
the state’s agricultural extensionists. They are more likely
than not to agree that growers and producers are interested
in using climate forecasts. Most of the state’s extension
personnel also think that farmers could use a climate
forecast to improve irrigation management and planting
schedules. A majority of the state’s extensionists thinks
that a freeze alert could be useful to them and the pro-
portion that thinks the forecast could be useful exceeds the
proportion that thinks any other forecast could be useful.
Most extensionists also think that a forecast of plant
moisture stress could be useful to help farmers schedule
irrigation. The key survey results are remarkably similar to
those from surveys of extension personnel at North Caro-
lina State University in early 2009 and University of
Florida in late 2004.
Keywords Agricultural extension � AgroClimate �Benefits of forecasts � Cochran and McNemar
statistics � Decision support tools � South Carolina
agriculture � Usefulness and uses of forecasts
Introduction
The El Nino-Southern Oscillation (ENSO) and its associ-
ated El Nino, Neutral, and La Nina phases influence the
seasonal climate in the southeastern USA (e.g., Fraisse
et al. 2006). As a result, ENSO creates production and
revenue risks for farmers in the region (e.g., Hansen et al.
1998; Nadolnyak et al. 2008). Farmers in the southeastern
USA and elsewhere can use information about climate
variability to change their crop management and, thereby
reduce these risks, and improve profitability (e.g., Jones
et al. 2000; Solow et al. 1998). Forecasts and historical data
about climate become more valuable to farmers if they are
S. R. Templeton (&)
John E. Walker Department of Economics, Clemson University,
Clemson, SC 29634-1309, USA
e-mail: [email protected]
M. Shane Perkins
Tri-County Technical College, P.O. Box 587, Pendleton,
SC 29670, USA
H. D. Aldridge
State Climate Office of North Carolina, North Carolina State
University, Raleigh, NC 27695-7236, USA
W. C. Bridges Jr.
Department of Mathematical Sciences, Clemson University,
Clemson, SC 29634, USA
B. R. Lassiter
Crop Science Department, North Carolina State University,
Raleigh, NC 27695-7620, USA
123
Reg Environ Change
DOI 10.1007/s10113-013-0522-7
presented as decision support tools in non-technical lan-
guage with an interactive computer interface (Breuer et al.
2008b; Fraisse et al. 2006; McCown et al. 2002).
Decision support tools on the website AgroClimate
(http://agroclimate.org/tools) initially became available for
South Carolina in 2010. In particular, County Yield Data-
base and Climate Risk for South Carolina became available
in May and September 2010. Eight additional tools were
operationalized between December 2010 and April 2011.
Peanut Leaf Spot Advisory was added in 2012. AgroCli-
mate’s decision support tools for the state cover drought,
other climate risks, crop yields, crop diseases, and degree
days and chill hours.
Most of AgroClimate’s decision support tools incorpo-
rate past and future information about climate. The tools
were developed with input from farmers and extension
agents in southeastern states of the USA other than South
Carolina, primarily Florida (e.g., Breuer et al. 2008b;
Fraisse et al. 2006). To assess the potential adoption of a
climatic information system for agricultural extension in
South Carolina and give feedback to developers of the
system, we have asked and provided initial answers to four
questions. First, are South Carolina’s growers and pro-
ducers interested in using climate forecasts? Second, what
are the potential uses of a forecast by the state’s farmers?
Third, which climate forecasts are potentially useful to
extension personnel in the state? Fourth, which decision
support tools in AgroClimate might be useful to farmers
and extension agents in the state?
Similar questions have been asked about decision sup-
port systems and climate forecasts for other states in the
USA or other countries. Answers to the questions have
been based on various methodologies, most of which have
not relied on structured surveys or extensively used sta-
tistical inference from survey data. For example, according
to three case studies (McCown et al. 2002), decision sup-
port systems enabled cotton producers in Australia to
substantially reduce their pesticide use and costs, wheat
farmers there to select a better cultivar, and livestock
producers in Texas to analyze the nutritional status of their
animals on pasture and, thereby, increase their profits
through changes in livestock feeding. The decision support
systems were all developed with input from farmers
(McCown et al. 2002). The case studies drew on researcher
recollection of their experiences with farmers in Australia
and Texas.
Anecdotal evidence from interviews, workshops, meet-
ings, and focus groups with farmers and extension agents in
Florida indicated that the potential for adoption of AgCli-
mate, the precursor of AgroClimate, to reduce risks related
to climate variability was encouraging (Fraisse et al. 2006).
Extension agents, row-crop farmers, and ranchers indicated
in 81 sondeos—informal, semi-structured discussions led
by teams of multi-disciplinary scholars—between March
1999 and March 2004 in north-central and other parts of
Florida that improved climate forecasts could help farmers
decide what, where, and when to plant (Breuer et al.
2008b). A majority of 26 extension agents from 13 counties
in southwest and west-central Florida agreed in sondeos
during March 2008 that they could benefit from knowing
which phase of ENSO was predicted and using AgClimate
to plan ahead and protect their crops from an anomalous
climate event (Breuer et al. 2008a). Selection of a crop or a
variety of a crop was the most commonly mentioned
potential use of a seasonal climate forecast by 38 farmers
from 21 southern Georgia counties during 31 semi-struc-
tured interviews between December 2006 and March 2007
(Crane et al. 2010). Changes in planting times and input
use were the second and third most frequently mentioned
potential uses of a forecast about seasonal climate (Crane
et al. 2010).
Two assessments of the usefulness of forecasts and uses
of climate forecasts have been based primarily on quanti-
tative summaries of answers to closed-ended questions
from two closely related, structured surveys. The original
36-question survey was created and conducted by
researchers with the Southeast Climate Consortium
(SECC) among extension agents with the University of
Florida (UF) during November and December 2004
(Cabrera et al. 2006). A subsequent 55-question survey,
which contained most of the original 36 questions, was
conducted by SECC researchers among extension agents
with North Carolina State University (NCSU) from March
6 through April 3, 2009 (Breuer et al. 2011). The extension
agents in both survey populations worked in agricultural
and natural resource management. Seventy-seven, or
86.5 %, of the 89 respondents from UF and 71, or 65.1 %,
of the 109 respondents from NCSU strongly agreed or
agreed that agricultural producers were interested in using
climate information (Breuer et al. 2011 and Cabrera et al.
2006). Moreover, planting schedules, irrigation manage-
ment, and nutrient management were selected by 68.5,
65.2, and 52.8 % of the respondents from UF as activities
that people with whom the respondents worked could use a
climate forecast to improve (Cabrera et al. 2006). Planting
schedules, harvest planning, and selection of a crop or
variety were selected by 85.3, 66.1, and 62.4 % of the
respondents with NCSU as activities that people with
whom the respondents worked could use a climate forecast
to improve (Breuer et al. 2011).
Statistical tests were conducted for differences in the
mean willingness of extension agents at NCSU to provide
advice about climate forecasts conditional on the agent’s
age and gender, work region, or clientele’s farm size
(Breuer et al. 2011). However, although unconditional
sample proportions differed and some exceeded 50 % in
S. R. Templeton et al.
123
the UF and NCSU survey data, no statistical test for
majorities or differences in unconditional probabilities
among extension agents was conducted. If majority support
for or relative popularity of a forecast or a potential use of a
climate forecast is to guide development of decision sup-
port tools, statistical tests about populations of extension
agents are important. Viewpoints of South Carolina’s ex-
tensionists about climate forecasts are also important for
future tool development.
In this study, we use survey data and the probability
distributions of binomial random variables to test whether a
majority of extensionists at Clemson University and, thus,
in South Carolina share viewpoints about the uses of cli-
mate forecasts and usefulness of forecasts. We also use a
chi-squared statistic of Cochran (Conover 1999) to test for
differences in proportions of Clemson’s extension person-
nel who select which forecasts are useful or which mana-
gerial activities that their clientele could improve with a
climate forecast. If differences exist, we then use the square
root of McNemar’s statistic (Conover 1999) to test whether
one proportion exceeds another. Scholars in multiple dis-
ciplines have used Cochran’s statistic (e.g., Van Berckelaer
et al. 2011) and McNemar’s statistic (e.g., Faravelli 2007)
for related hypothesis tests. We also use focus-group data
to provide preliminary insights into the potential usefulness
of AgroClimate’s decision support tools, but the data can-
not be used for statistical inference.
Methodology
Focus-group data
Our assessment of the potential usefulness of decision
support tools in AgroClimate is primarily based on data
from focus-group participants. Participants were recrui-
ted from people who attended a SECC workshop about
AgroClimate on January 14, 2011 at Clemson Univer-
sity’s Pee Dee Research and Education Center in Flor-
ence, South Carolina. Twenty-two people attended the
workshop and received 50 min of instruction about South
Carolina’s climate from State Climatologist Hope Mizz-
ell. They then received 75 min of instruction from Clyde
Fraisse, Climate Extension Specialist with the University
of Florida, about the County Yield Database, Climate
Risk, and other decision support tools in AgroClimate for
South Carolina.
Two focus groups were conducted after lunch for
1 hour, led by Templeton and Jessica Whitehead, then
Regional Climate Extension Specialist for S.C. Sea Grant.
Attendees had been notified before the workshop about the
focus groups and at the end of the workshop were
encouraged, but not required, to participate. Perkins and
Lassiter helped to develop the focus-group questions, pre-
pare materials, and record responses. Mizzell and Fraisse
provided technical expertise for each group. Nine partici-
pants were preassigned to each focus group for diversity:
three extension agents, two experiment-station or Agri-
cultural-Research-Service researchers, one farmer, and
three others, such as a crop loan specialist, plant inspector,
crop consultant, other extension agent, or other researcher.
After introducing themselves and their backgrounds, par-
ticipants shared their impressions of AgroClimate. Each
participant was then asked to write and publicly state their
answers to three questions: (1) Which three decision sup-
port tools in AgroClimate would be most useful to exten-
sion agents? (2) Which three tools in AgroClimate would
be most useful to farmers? (3) What is missing in Agro-
Climate, or what new decision support tool should be
developed? In one of the groups, ‘decision support tools in
AgroClimate’ meant existing and yet-to-be-developed
tools. Each participant’s six votes—the three most useful
forecasts for extension agents and the three for farmers—
were publicly tallied.
Survey data
Our assessments of interest in and potential uses of climate
forecasts and usefulness of forecasts for South Carolina are
based on analysis of data from a structured survey of
Clemson University’s extension personnel conducted in
January and February 2011. The survey was adapted from
the one conducted in North Carolina (Breuer et al. 2011).
Our survey population consisted of 171 employees, 154
permanent and 17 temporary, who were extension associ-
ates, agents, or specialists for Clemson University. The
Small Farm Assistance and Outreach Program of South
Carolina State University, the state’s 1890 land-grant
institution, had six extension personnel in 2011, according
to Dr. Edoe Agbodjan, the program’s director. Thus, our
survey population represented almost 97 % of South Car-
olina’s agricultural extensionists.
Procedures of Dillman et al. (2009) were followed to
enhance the quality and quantity of survey responses. The
Experiment Station and Extension Directors endorsed the
survey and provided contact information about extension
personnel. An email preview of the upcoming survey was
sent on December 20, 2010 to permanent employees and a
request to participate was emailed on January 6, 2011. A
reminder to complete the survey was sent on January 13,
2011, 1 day before the AgroClimate workshop, to several
extension personnel who had registered for the workshop.
A follow-up request to complete the survey was sent by the
Experiment Station and Extension Directors on January 28,
2011 to all permanent employees who had not attended the
workshop. A final request to complete the survey was sent
Usefulness and uses of climate forecasts
123
on February 3, 2011, roughly 4 weeks after the initial
invitation, to all permanent employees who had not atten-
ded the workshop.
Similar procedures were followed for extension per-
sonnel who were temporary employees and did not attend
the workshop. They were sent a preview of the survey on
February 2, 2011 and an invitation to participate in the
survey on February 10, 2011. A follow-up email from the
Experiment Station and Extension Directors was sent out to
the temporary extension personnel on February 18, 2011.
The final survey reminder was sent out to them on February
24, 2011.
Forty-nine, or 29 %, of Clemson’s extension personnel
responded. The response rate was within the range of
reported rates of response to various online surveys and
near the mean response rate, 33 % (Nulty 2008). Males
accounted for 81 % of the respondents but 63 % of the
survey population. Twenty-six, or 53.1 % of all, respon-
dents were extension agents. Eighteen, or 36.7 % of all,
respondents were extension specialists. Five respondents,
or 10.2 %, were extension associates. Extension agents,
specialists, and associates account for 63.2, 28.1, and 8.8 %
of Clemson’s extension personnel. In short, extension
associates were proportionally represented, males and
specialists slightly over proportionally represented, and
extension agents slightly under proportionally represented
in the sample.
Variables from survey data
Categorical responses to two statements and a question in
the survey are the sources of data for variables. The first
statement is, ‘‘In my opinion, growers and producers
(including forest owners, livestock producers, etc.) in my
region are interested in using climate forecasts. (1) strongly
agree, (2) agree, (3) neither agree nor disagree, (4) dis-
agree, or (5) strongly disagree.’’ Let Yr = one if respondent
r selects ‘strongly agree’ or ‘agree’ and zero if not and let
Y �P49
r¼1 Yr be the number of respondents who select
‘strongly agree’ or ‘agree.’
The second statement is, ‘‘People I work with can use
climate forecasts to improve … (Check all that apply.).’’
The managerial activities that might be improved with
climate forecasts are these: (1) planting schedules, (2)
allocation of land to crops or activities, (3) labor man-
agement, (4) harvest planning, (5) waste management, (6)
nutrient management, (7) irrigation management, (8)
marketing, (9) variety or crop selection, (10) spacing or
stand density, (11) integrated pest management, and (12)
other. Let Ira equal one if respondent r checks managerial
activity a and zero if not and Ia �PR
r¼1 Ira be the number
of respondents who check activity a.
The question is ‘‘Which of these forecasts could be
useful to you? (Check all that apply.).’’ The potentially
useful forecasts are these: (1) freeze alert, (2) wildfire risk,
(3) climate risk, (4) disease risk, (5) El Nino or La Nina
phase, (6) growing degree days, (7) cooling–heating degree
days, (8) lawn and garden moisture index, (9) yield risk,
(10) chill hours accumulation, (11) plant moisture stress,
(12) livestock heat stress index, and (13) other. Let Urf = 1
if respondent r checks forecast f and 0 if not and Uf �P49
r¼1 Urf be the number of respondents who check forecast
f.
Statistical methods for hypothesis tests
The random variables Y, Ia, and Uf have binomial distri-
butions in which PY , PIa, and PUf
are, by definition, the
probabilities that a extensionist thinks farmers are inter-
ested in using climate forecasts, the people with whom he
or she works could use a climate forecast to improve
activity a, and forecast f could be useful to him or her. Of
course, each of the probabilities is also a population pro-
portion and a hypothesis test of a probability in excess of
0.50 is equivalent to a test for a majority. Consider, for
example, the null hypothesis that a majority of extension-
ists think farmers are not interested in using climate fore-
casts. Let y be the actual number of respondents who
strongly agreed or agreed that farmers were interested. If
the probability that at least y respondents would do so is
small enough, then we reject the null and conclude that a
majority of extensionists think farmers are interested. The
binomial probability that at least y respondents would
strongly agree or agree is PrðY � yÞ ¼P49
j¼y
49
j
� �
0:549
and was calculated in a spreadsheet with a macrofunction
for combinations. Hypotheses about PIaand PUf
exceeding
0.50 were tested with probabilities similarly calculated in a
spreadsheet.
Do the probabilities that a climate forecast could
improve specific managerial activities differ? Does the
probability that an extensionist thinks a forecast could be
useful differ from the probability that he or she thinks all
other forecasts could be? If so, for which pairs of uses or
forecasts do the probabilities differ? Empirical evidence to
answer these questions cannot come from test statistics that
require independently drawn samples from which sample
proportions are calculated. The survey and the sampling
procedure—the same respondents were asked about all
potential uses of a forecast and all potentially useful fore-
casts—represent an experimental design called randomized
complete block (Conover 1999). Each respondent is a
randomized ‘block,’ each forecast or use of a forecast is a
‘treatment,’ and each forecast or use of a forecast that a
S. R. Templeton et al.
123
respondent checks is a treatment ‘success.’ Instead, evi-
dence can come from Cochran’s statistic and the square
root of McNemar’s statistic (Conover 1999).
Cochran’s statistic to test whether the probabilities of an
extension client using a climate forecast to improve at least
two managerial activities differ is CSI ¼ 11�
10
P11
a¼1Ia� I
11ð Þ2P49
r¼1Ir 11�Irð Þ
, in which Ir �P11
a¼1 Ira is the number of
managerial activities that respondent r indicates could be
improved by a climate forecast and I �P49
r¼1
P11a¼1 Ira is
the number of times that all respondents check any activity
as capable of being improved with a climate forecast. The
subscript a runs to 11 because the twelfth activity, ‘other,’
is too vague. Given the null hypothesis of universal
equality and a ‘large’ sample, the distribution of CSI is
approximately chi-squared with 10 degrees of freedom. We
reject HIall0 : PI1
¼ PI2¼ � � � ¼ PI11
for possible improve-
ments in all eleven activities in favor of HIall1 : PIa
6¼ PIb;
a 6¼ b for at least two different activities, if Pr v210
�
�CSI� 0:05.
The hypothesis that PIa, the proportion of extensionists
who think people with whom they work will use a forecast
to improve managerial activity a, exceeds PIb, the pro-
portion of extensionists who think that their farm clients
will use a forecast to improve managerial activity b, is
tested with the square root of McNemar’s statistic,ffiffiffiffiffiffiffiffiffiMSIp
¼ IajðIb¼0Þ½ �� IbjðIa¼0Þ½ �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiIajðIb¼0Þ½ �þ IbjðIa¼0Þ½ �
p . InffiffiffiffiffiffiffiffiffiMSIp
, IbjðIa ¼ 0Þ �P49
r¼1 IrbjðIra ¼ 0Þ½ � is the number of respondents who
check that a climate forecast could improve activity b but
not activity a and IajðIb ¼ 0Þ �P49
r¼1 IrajðIrb ¼ 0Þ½ � is the
number who indicate a climate forecast could improve
activity a but not activity b. Given a ‘large’ sample and the
null hypothesis that probabilities for the two different
activities are equal, the square root of McNemar’s statistic
is approximately distributed as standard normal. We reject
HItwo0 : PIa
�PIbfor the two activities in favor of HItwo
1 :
PIa[ PIb
if Pr Z�ffiffiffiffiffiffiffiffiffiMSIp� �
� 0:05.
Cochran’s statistic to test for inequality among the
twelve probabilities that an extensionist thinks forecasts
could be useful is CSU ¼ 12 � 11
P12
f¼1Uf�U
12ð Þ2P49
r¼1Ur 12�Urð Þ
, in which
Ur �P12
f¼1 Urf , U �P49
r¼1
P12f¼1 Urf , and f runs to 12
because the thirteenth forecast, ‘other,’ is too vague. The
square root of McNemar’s statistic to test whether PUf, the
probability that an extensionist thinks forecast f could be
useful, exceeds PUg, the probability that he or she thinks
forecast g could be useful, isffiffiffiffiffiffiffiffiffiffiffiMSUp
¼Uf jðUg¼0Þ½ �� UgjðUf¼0Þ½ �ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Uf jðUg¼0Þ½ �þ UgjðUf¼0Þ½ �p , where Uf jðUg ¼ 0Þ �
P49r¼1 Urf j�
ðUrg ¼ 0Þ� and UgjðUf ¼ 0Þ �P49
r¼1 UrgjðUrf ¼ 0Þ� �
.
Given a ‘large’ sample and the null hypotheses that all
twelve probabilities and two specific probabilities are
equal, the respective distributions of CSU andffiffiffiffiffiffiffiffiffiffiffiMSUp
are
approximately chi-squared with 11 degrees of freedom and
standard normal. The statistics were calculated and alter-
native hypotheses tested with JMP�, Ver. 9 (1989–2012)
software.
Results
Focus group
County Yield Database and Climate Risk were most and
second most likely among decision support tools in Agro-
Climate to be useful to extension agents in South Carolina,
according to focus-group votes (Fig. 1). Agricultural Ref-
erence Index for Drought (ARID) and Strawberry Advisory
System received the third most and same number of votes
for usefulness to extension agents in the state (Fig. 1).
Climate Risk and Yield Risk were equally and most likely to
be useful to farmers. County Yield Database and Agricul-
tural Reference Index for Drought were the third and fourth
most popular tools for usefulness to farmers. An irrigation
scheduler was identified as the most important tool yet to
be developed. Smart-phone applications for disease
advisories were the next priority for development.
Survey
Seventy-one percent of the respondents agreed or strongly
agreed that growers and producers in their region were
interested in using climate forecasts. The probability that at
Fig. 1 Focus-group votes for potential usefulness of decision support
tools to farmers, extension agents, or both
Usefulness and uses of climate forecasts
123
least 35 of 49 extensionists would agree is 0.0019, so the
null of no majority is rejected.
Six managerial activities that extension’s clientele could
improve with a climate forecast were checked by at least
half of the respondents (Table 1). However, the null
hypothesis of no majority is strongly and weakly rejected
for only two most checked activities. In particular, the
probabilities that irrigation management and planting
schedules are at least as popular as the respondents indi-
cated, given the null hypotheses, were 0.011 and 0.076.
Moreover, the Cochran statistic under the null hypothesis
of equal proportions is 75.65. The associated p value is less
than 0.001 and the null is rejected. Thus, the probabilities
that extension clientele can use a climate forecast to
improve eleven management activities in South Carolina
are not all equal. As a result, 55 pairwise comparisons were
made of potential uses of a climate forecast (Table 1).
The probability that an extensionist thinks that farmers
could use a climate forecast to improve irrigation manage-
ment is not necessarily greater than the probability that he or
she thinks that farmers could use a climate forecast to
improve planting schedules or harvest planning (Table 1).
Farmers are also more likely to use forecasts to improve
allocation of land to crops or activities, selection of crops or
crop varieties, and integrated pest management than to
improve the spacing or stand density of planted trees, mar-
keting, labor management, or waste management (Table 1).
The probabilities that a climate forecast could improve
nutrient management and the spacing or stand density of
planted trees do not necessarily differ but exceed the prob-
abilities that forecasts could improve marketing, labor
management, and waste management, which are activities
least likely to be improved by a climate forecast (Table 1).
Five forecasts were checked by a majority of respon-
dents as potentially useful to them (Table 2). However, the
null hypothesis of no majority in the population is rejected
for only the two most checked forecasts. The probabilities
that a freeze alert and a forecast of plant moisture stress are
at least as popular as the respondents indicated, given the
null hypotheses, were 2.86 9 10-8 and 0.043. Moreover,
the Cochran statistic under the null hypothesis of equal
proportions is 72.75. The associated p value is less than
0.001 and the null of universal equality is rejected. In short,
the proportions of Clemson extension personnel who think
various forecasts are useful are not all equal. As a result, 66
comparisons of the potential usefulness of two forecasts
were made.
The probability that a freeze alert could be useful is
significantly greater than the probability that any other
forecast could be useful (Table 2). Although less likely to
be useful than a freeze alert, forecasts about plant moisture
stress, the El Nino or La Nina phase, growing degree days,
and chill hours accumulation are more likely to be useful
than the five forecasts with the lowest sample frequencies
of being useful (Table 2). A minority of extensionists
thinks that forecasts of disease, wildfire, and yield risks and
a livestock heat stress index would be useful to them.
Discussion
A top vote getter in the focus groups Climate Risk displays
probabilistic forecasts of the upcoming ENSO phase and
information about historical monthly temperatures and
precipitation in each county for each ENSO phase and all
phases combined. Climate Risk might have tied for most
popular tool because the focus-group participants had just
learned about the relatively accurate forecasts of the ENSO
phase in the southeastern USA, particularly during fall and
winter months (Hansen et al. 1998), and had received
detailed hands-on instruction about how to use the tool. In
contrast, most extensionists do not think a forecast of cli-
mate risk could be useful for the possible reason that most
do not know about it. Respondents were not provided a
description of any forecast in the survey.
County Yield Database, the other top vote getter, was
also described and used in an instructional exercise for
almost as much time as Climate Risk was in the workshop.
A display of historical county-level crop yields, County
Yield Database is not a forecast, however, and, thus, was
not in the survey. The three other most popular tools—
Yield Risk, ARID, and the Strawberry Advisory System—
were also introduced during the workshop. None of the
three was operational for South Carolina at the time of the
workshop, however, and only yield risk was included in the
survey as a possibly useful forecast.
The popularity of decision support tools based on votes
of 18 focus-group participants cannot be generalized.
However, the popularity of forecasts based on survey
responses can be generalized with caution. For example,
most South Carolina extensionists think that farmers would
be interested in using climate forecasts and, in particular,
using one to improve irrigation management or planting
schedules. This generalization reflects the statistical evi-
dence already presented and additional survey evidence:
irrigation planning and planting dates were the first and
second most frequently checked managerial activities—by
36 and 35 of the 49 respondents—that crop growers could
improve if they had better climate forecast information.
Most extension agents with NCSU and the UF tend to
concur (Breuer et al. 2011; Cabrera et al. 2006). In par-
ticular, farmer use of a climate forecast to improve planting
schedules was most frequently selected by NCSU and UF
agents. Irrigation management was the second and fourth
most selected use of a climate forecast by UF and NCSU
agents.
S. R. Templeton et al.
123
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Usefulness and uses of climate forecasts
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S. R. Templeton et al.
123
Extensionists think that farmers are interested in using
forecasts to improve management of irrigation and planting
because, we hypothesize, the benefits to farmers of using
the forecast are likely to exceed the costs—time, money,
and mental effort of farmers—of learning about and
applying it. Our hypothesis is consistent with the concept
of a valuable forecast, i.e., one that generates incremental
benefits (Murphy 1993). A climate forecast is more likely
to be valuable, or beneficial, for a strategic, preseason
decision than for a tactical, during-season decision (Fraisse
et al. 2006). A farmer’s planning for seasonal irrigation
demand and scheduling of plantings are related strategic,
preseason decisions. The seasonal demand for irrigation
depends on seasonal rainfall and a crop’s growth stage,
which, in turn, depends on the planting date. In contrast,
only a minority of extension personnel thinks farmers
could use a climate forecast to improve marketing, labor
management, or waste management because, we hypothe-
size, they perceive, rightly or wrongly, that the activity
does not depend much on seasonal climate.
A freeze alert is also likely to be useful to a majority of
extension personnel in and beyond South Carolina. Freeze
alerts were the most frequently checked forecast, by size-
able margins, for usefulness to extension agents in Florida
(Cabrera et al. 2006) and North Carolina (Breuer et al.
2011). The high probabilities that freeze alerts could be
useful might reflect our survey having been conducted
during a relatively cold winter and the other two surveys
having been conducted in late fall and late winter. How-
ever, freezing of crops can cause substantial economic
damages. For example, a cold-air outbreak between April
6–10 and well-below-freezing temperatures on April 8,
2007 contributed to losses of approximately 79 % for
peach, 85 % for apple, and 39 % for winter-wheat harvests
and $39.3 million in farmgate revenues in South Carolina
(NOAA-USDA 2008).
Most South Carolina extension personnel also think that
a forecast of plant moisture stress could be useful to them,
although the proportion that thinks the forecast could be
useful is statistically less than the proportion that thinks a
freeze alert could be useful. Extension personnel could
teach farmers how to use a forecast of plant moisture stress
to schedule irrigation. Irrigation has become more impor-
tant for South Carolina’s agriculture. The area of irrigated
cropland in the state increased from 35,362 ha. in 1997 to
37,148 ha. in 2002 to 49,944 ha. in 2007 (NASS 2004,
2009). Irrigated area’s share of total harvested area
increased from 5.04 % in 1997 to 6.68 % in 2002 to
7.95 % in 2007. Participants in the AgroClimate workshop
in early 2011 reported that irrigation was still expanding in
the state.
A freeze alert and a forecast of plant moisture stress
could be useful to most extensionists because the forecasts
are short-term and, as such, are generally more accurate
than most climate forecasts. A short-term, or weather,
forecast can help an extensionist teach or advise farmers
when to take tactical actions—such as when to protect a
crop from imminent freeze damage or when next to irrigate
a crop for reduction in plant moisture stress—that create
immediately tangible benefits whereas some long-term, or
climate, forecasts are less likely to do so. In contrast,
Freeze Risk Maps ranked only sixth in potential usefulness
among AgroClimate’s decision support tools in the focus
groups (Fig. 1) for the possible reason that the county-level
maps could only help extension agents and farmers ascer-
tain, in light of the forecasted ENSO phase, the odds that
protection of crops from freeze damage during an
upcoming winter would be needed but not help them
decide when during the winter such action should be taken.
Conclusion
AgroClimate lacks decision support tools for irrigation
management and freeze protection in the Carolinas. A
freeze alert and a map of freeze probabilities, the displays
of which would vary with the ENSO phase, are also likely
to be useful to farmers of various crops, not just a majority
of extension personnel. The information to create such
tools already exists.
A tool that incorporates a forecast of plant moisture
stress for irrigation scheduling is also likely to be useful to
South Carolina’s farmers, not just extensionists. Most
irrigators rely on rules of thumb for scheduling or sched-
ulers that utilize data from neighboring states whose agro-
climatic conditions might not apply to the state (Farahani
et al. 2008). However, the lower and upper threshold
temperature functions for different crops in South Carolina
must first be determined through research if such sched-
ulers are to be developed (Farahani et al. 2008).
If probabilistic forecasts of the ENSO phase in an
upcoming spring and summer are to be useful for managing
irrigation and scheduling of planting, the forecasts need to
have sufficient accuracy, or quality (Murphy 1993), and the
impact of the ENSO phase on precipitation needs to be
sufficiently discernible. The scientific knowledge for such
accuracy and discernment in South Carolina might not yet
be adequate, however. The ENSO phase has been more
difficult to accurately predict for spring and summer than
other seasons in the southeastern USA (e.g., Barnston et al.
2012; Hansen et al. 1998). Also, although the ENSO signal
clearly affects the climate of the coastal plain of the Car-
olinas (Hansen et al. 1998), its effects on the climate of the
Sandhill and Piedmont regions are small or not yet well
understood. Moreover, recent research of Gail Wilkerson,
Professor of Crop Sciences at North Carolina State
Usefulness and uses of climate forecasts
123
University, suggests that the impact of El Nino or La Nina
on rainfall in April and May might not be discernible in the
coastal plain of the Carolinas because factors other than
ENSO tend to dominate the inter-annual variability during
these months.
Our assessment might be limited by the scope and nature
of our data. Our focus groups and survey population
excluded South Carolina State University’s extension per-
sonnel, who represented about 3 % of the state’s exten-
sionists. Thus, our results and interpretations might not
apply to them and their clients. Most of their clients are
black or African-American farmers, who represented 7 %
of South Carolina’s farmers in 2007 (NASS 2009). More-
over, our survey data represent stated preferences about
forecasts, most of which have not yet been incorporated
into decision support tools for South Carolina. Stated
preferences might change as extension personnel learn
about climate forecasts that have already been incorporated
into decision support tools, such as Climate Risk.
Our conclusions are based primarily on the results of
statistical tests for majorities and differences in uncondi-
tional probabilities. However, respondents might have been
more enthusiastic about forecasts than non-respondents.
Moreover, the gender, age, and other characteristics of
extensionists might also affect their assessments of a
forecast (e.g., Breuer et al. 2011; Cabrera et al. 2006). For
example, if extensionists are younger or more computer
literate than others are, then the likelihood that a forecast is
potentially useful might be higher for these subpopulations
than for others. Analysis of conditional probabilities of
forecasts being useful or managerial activities being
improved by forecasts is important for future research.
Finally, a forecast that is likely to be useful or a managerial
activity that is likely to be improved by a climate forecast is
not necessarily a forecast or use of it that will be most bene-
ficial, on net, to farmers or society. For example, the expected
net benefits of an index of livestock heat stress, which is not be
regarded by many as potentially useful, might exceed the
expected net benefits of a forecast of climate risk. Estimation
of the discounted total benefits and costs of the forecasts to
farmers, consumers, and others would be required. None-
theless, if potentially widespread adoption matters to those
who develop decision support tools for extension, our focus-
group and survey results have provided a baseline of com-
plementary information about potential usefulness of fore-
casts and uses of climate forecasts.
Acknowledgments Our initial research was conducted under a
subcontract with Florida State University for the project ‘Decision
Support System for Reducing Agricultural Risks Caused by Climate
Variability,’ which was funded by the United States Department of
Agriculture’s Cooperative State Research, Educational, and Extension
Service. The paper was written and revised as part of a subcontract
with the University of Florida for the project ‘Climate Variability to
Climate Change: Extension Challenges and Opportunities in the
Southeast USA,’ which is funded by Competitive Grant No.
2011-67003-30346 from the USDA’s National Institute of Food and
Agriculture. We thank George Askew, Nelle Bridges, Teresa Kelley,
Eleanor Massey, and Steve Meadows for help with survey adminis-
tration; Kathryn Boys, Rebecca Davis, Patricia DeHond, Hamid Fa-
rahani, Bruce Fortnum, Mandy Stephan, and Jessica Whitehead for
help with workshop arrangements; and Ryan Boyles, Norman Breuer,
Wolfgang Cramer, Clyde Fraisse, Keith Ingram, Jim Jones, Vasu
Misra, Tom Mroz, Jim O’Brien, Gail Wilkerson, and two anonymous
reviewers for their comments. We are responsible for any remaining
errors. Aldridge and Bridges share third authorship.
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