The Value of Plant Disease Early-Warning Systems: A Case Study of USDA's Soybean Rust Coordinated Framework
by Michael Robbins, David Schimmelpfennig
, Elizabeth Ashley, Michael Livingston, Mark Ash
, and Utpal Vasavada
Economic Research Report No. (ERR-18) 46 pp, 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
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