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The Value of Plant Disease Early-Warning Systems:
A Case Study of USDA's Soybean Rust Coordinated Framework
Michael J. Roberts, David Schimmelpfennig, Elizabeth
Ashley, Michael Livingston, Mark Ash, and Utpal Vasavada
Economic Research Report No. (ERR-18), April 2006
Early-warning systems for plant diseases are valuable when
the systems provide timely forecasts that farmers can use to
mitigate potentially damaging events through preventative management.
For example, soybean rust (SBR), a soybean fungus which entered
the United States in late 2004, posed a new, uncertain, and
potentially large threat at the beginning of the 2005 U.S. soybean
season. Farmers anticipated markedly reduced soybean yields
on fields infected with SBR, but with sufficient notice, they
could treat the fields in advance with preventative fungicides,
a costly, but prudent, measure.
What Is the Issue?
In 2005, USDA developed an early-warning system that provides
real-time, county-level forecasts of soybean rust. This system
provides farmers, crop consultants, and others with interests
in the U.S. soybean crop timely forecasts of SBR infestations
that could sharply reduce soybean yields. Forecasts and recommended
management activities are provided via a publicly accessible
website, the first time a web-based system has been used for
this purpose. The information on the website is developed through
a large coordinated framework that involves many government
and nongovernment organizations that regularly collect samples
from fields, test them, and incorporate them into forecasting
models. But how valuable is the information provided by the
framework? This question has become particularly salient in
light of modest outbreaks of SBR in 2005. This study uses the
SBR system as a case study to determine the effectiveness of
such early-warning systems. The answer will aid decisions on
future investments in this system and perhaps others like it.
What Did the Study Find?
The value of the framework’s information depends on many
factors, particularly farmers’ perceived risk at the beginning
of the season of SBR infection and the accuracy of the system’s
forecast. These factors cannot be precisely quantified, but
our analysis shows that, although the value of information from
the system varies somewhat geographically, overall the system's
value has been substantial. Even if forecasts are poor, resolving
only 20 percent of SBR infection uncertainty for all fields
planted with soybeans, the system’s value is an estimated
$11 million in farmer profits in the first year. If forecasts
resolve 80 percent of infestation uncertainty, the estimated
value is $299 million. Our analysis suggests that the value
of the information in 2005 likely exceeds reported costs of
developing the information.
The study also analyzes two more subtle features that affect
estimated information values: anticipated price shocks in the
event of large rust outbreaks and soybean farmers’ aversion
to risk. We found that both of these factors reduce the largest
estimated values and increase the smallest ones, but the magnitude
of the effects are modest relative to the perceived forecast
quality. The large potential benefits of the framework suggest
that similar programs for other crop pests can be cost effective
if, as in the case of soybean rust, preventative action can
strongly mitigate damages in the event an outbreak.
How Was the Study Conducted?
The study applies conceptual methods from decision science
to evaluate how much expected profits increase if farmers are
able to fine-tune their rust management decisions in response
to SBR forecasts. These methods are combined with USDA data
on historical soybean yields, data from USDA’s Agricultural
Resource Management Survey, estimated soybean rust damages from
Brazil and Paraguay, and spore dispersion estimates based on
an aerobiology analysis and historical experience with wheat
stem rust. Information values were calculated over a broad range
of assumptions because some of the parameters were not estimable
and some parameter estimates were uncertain.
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