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Most of the genetically engineered crops that are commercially
available have been developed to carry herbicide-tolerant
or insect-resistant genes. Crops carrying herbicide-tolerant
genes were developed to survive certain herbicides that
previously would have destroyed the crop along with the
targeted weeds. Farmers thus can choose from a broader
variety of herbicides to control weeds. The most common
herbicide-tolerant crops are Roundup Ready (RR) crops
resistant to glyphosate, a herbicide effective on many
species of grasses, broadleaf weeds, and sedges. Glyphosate
tolerance has been incorporated into cotton, corn, soybeans,
and canola. Other genetically modified herbicide-tolerant
crops include Liberty Link (LL) corn resistant to glufosinate-ammonium,
and BXN cotton resistant to bromoxynil. There are also
traditionally bred herbicide-tolerant crops, such as corn
resistant to imidazolinone (IMI) and sethoxydim (SR),
and soybeans resistant to sulfonylurea (STS).
Contents
Extent of Adoption of
Commercially Available Varieties of Genetically Engineered
Crops By U.S. farmers
Adoption
of herbicide-tolerant crops has been particularly
rapid. Herbicide-tolerant soybeans became available to
farmers for the first time in limited quantities in 1996,
but use expanded to about 17 percent of soybean acreage
in the major States surveyed in 1997, and to more than
50 percent in 2000 Herbicide-tolerant cotton expanded
from 10 percent of surveyed acreage in 1997 to 26 percent
in 1998, and reached 46 percent in 2000.
Bt crops containing the gene from a soil bacterium, Bacillus
thuringiensis, are the only insect-resistant crops commercially
available. The bacteria produce a protein that is toxic
when ingested by certain Lepidopteran insects. Crops containing
the Bt gene are able to produce this toxin, thereby providing
protection against Lepidopteran insects throughout the
entire plant. Bt has been built into several crops, the
most important being corn and cotton.
- Bt cotton is primarily effective in controlling the
tobacco budworm, the bollworm, and the pink bollworm.
Use of Bt cotton expanded rapidly, reaching 15 percent
of cotton acreage in 1996 and about 35 percent in 2000
(see Farmer-Reported
Genetically Modified Varieties", pages 28-29.
- Bt corn provides protection from the European corn
borer and, to a lesser extent, the corn earworm, the
southwestern corn borer, and the lesser cornstalk borer.
The Environmental Protection Agency (EPA) approved Bt
corn in August 1995 and its use grew from about 1 percent
of planted corn acreage in 1996 to 19 percent in 1998.
It peaked at about 26 percent in 1999 before falling
to 19 percent in 2000.
Farmer Motivations for
Adopting Genetically Engineered Crops
The majority of farmers adopting genetically engineered
cotton and soybeans with pest management traits (ranging
from 54 to 76 percent of surveyed adopters) indicated
that they adopted mainly to “increase yields through
improved pest control.” The second major reason was
“to decrease pesticide costs” (19-42 percent
of adopters). All other reasons combined (such as increased
planting flexibility, and environmental benefits) were
cited by 3-15 percent of adopters.
These results confirm other adoption studies showing
that expected profitability positively influences the
adoption of agricultural innovations. Hence, factors expected
to increase profitability by increasing revenues per acre
(price of the crop times yield) or reducing costs are
expected to promote adoption. Given that an objective
of pest management in agriculture is to reduce crop yield
losses, there is a high incentive for innovations that
reduce these losses.
Relationship Between Crop Yields
and Genetically Engineered Crops Yields
It is difficult to estimate the farm-level effect of
genetically engineered (GE) crops on yields because impacts
vary with the crop and technology examined. Yields also
depend on locational factors such as soil fertility, rainfall,
and temperature. The physical environment of the farm
(e.g., weather, soil type) affects profitability directly
through increased fertility and indirectly through its
influence on pests.
In addition, there is the problem of self-selection that
arises because farmers are not assigned randomly to the
two groups (adopters and nonadopters), but make the adoption
choices themselves. Therefore, adopters and nonadopters
may be systematically different and these differences
may manifest themselves in farm performance and could
be confounded with differences due purely to adoption.
GE crops do not increase the yield potential of a hybrid.
In fact, yield may even decrease if the varieties used
to carry the herbicide-tolerant or insect-resistant genes
are not the highest yielding cultivars. However, by protecting
the plant from certain pests, GE crops can prevent yield
losses compared with non-GE hybrids, particularly when
infestation of susceptible pests occurs. This effect is
particularly important in the case of Bt crops. Before
the commercial introduction of Bt corn in 1996, the European
corn borer (ECB) was only partially controlled using chemical
insecticides. The economics of chemical use were not always
favorable and timely application was difficult. For these
reasons, farmers often accepted yield losses (of about
3 to 6 percent per one corn borer per plant, depending
on the stage of plant development) rather than incur the
expense of chemical pesticides to treat the ECB.
An ERS study estimated
the impact of adopting GE crops using 1997 survey data.
Herbicide-tolerant soybeans and cotton and Bt-enhanced
cotton crops were modeled individually. In each model,
pest infestation levels, other pest management practices,
crop rotations, tillage, and self-selection were controlled
for statistically. Geographic location was included as
a proxy for soil, climate, and agricultural practice differences
that might influence impacts of adoption.
Results of such modeling can be interpreted as an elasticity—the
change in a particular impact (yields, pesticide use,
or profits) relative to a small change in adoption of
the technology from current levels. The results can be
viewed in terms of aggregate impacts across the entire
agricultural sector as more producers adopt the technology,
or in terms of a typical farm as they use the technology
on more of their land. As with most cases in economics,
the elasticities estimated in the quantitative model should
only be used to examine small changes (say, less than
10 percent) away from current levels of adoption.
The study shows that adoption of herbicide-tolerant cotton
led to significant increases in yields. The elasticity
of yields with respect to the probability of adoption
of herbicide-tolerant cotton is +0.17. That is, an increase
of 10 percent in the adoption of herbicide-tolerant cotton
led to a 1.7-percent increase in yields. Similarly, the
adoption of Bt cotton in the Southeast increased yields
significantly (elasticity of yields is +0.21). On the
other hand, increases in adoption of herbicide-tolerant
soybeans led to small (but significant) increases in yields
(elasticity of yields is 0.03).
Relationship between Adoption
of Genetically Engineered Crops and Pesticide Use
Data on pesticide use by producers who did and did not
adopt genetically engineered crops are available, but
many factors other than adoption affect pesticide use
making simple comparisons suspect. In addition, the changing
mix of pesticides that accompanies adoption complicates
the analysis, because characteristics like toxicity and
persistence in the environment vary across the pesticides
used.
Several perspectives on estimating changes in pesticide
use associated with adoption of GE crops are available
from an ERS
analysis of survey data using three statistical methods,
- Same-year differences. Compares mean
pesticide use between adopters and nonadopters within
1997 and within 1998 for a given technology, crop, and
region, and applies that average to total acres producing
each crop in each year.
- Year-to-year differences. Estimates
aggregate differences in pesticide use between 1997
and 1998, based on increased adoption of GE crops between
those 2 years and average total pesticide use by both
adopters and nonadopters.
- Regression analysis. Estimates differences
in pesticide use between 1997 and 1998, with an econometric
model controlling for factors other than GE crop adoption
that may affect pesticide use.
Changes in pesticide acre-treatments resulting from adoption
range from -6.8 million to -19 million acre-treatments
across the three estimation methods. Reductions in pounds
of active ingredients vary more widely, from a net drop
of just 0.3 million pounds in 1997 (using the same-year
method) to 8.2 million pounds (using the year-to-year
method). Because the results include only statistically
significant differences in pesticide use by adopters and
nonadopters, many relatively small differences in particular
regions were not included, thus underestimating overall
differences.
Measuring pesticide use in pounds of active ingredient
implicitly assumes that a pound of any two ingredients
has equal impact on human health and/or the environment.
However, the more than 350 pesticide active ingredients
vary widely in toxicity per unit of weight and in persistence.
Consider, for example, the adoption of herbicide-tolerant
soybeans, which leads to the substitution of glyphosate
herbicides for previously used herbicides. Based on regression
results, an estimated 5.4 million pounds of glyphosate
is substituted for 7.2 million pounds of other synthetic
herbicides, such as imazethapyr, pendimethalin, and trifluralin.
Glyphosate has a half-life in the environment of 47 days,
compared with 60-90 days for the herbicides it commonly
replaces. The herbicides that glyphosate replaces are
3.4 to 16.8 times more toxic, according to a chronic risk
indicator based on the EPA reference dose for humans.
Thus, the substitution enabled by genetic modifications
conferring herbicide tolerance on soybeans results in
glyphosate replacing other synthetic herbicides that are
at least three times as toxic and that persist in the
environment nearly twice as long as glyphosate.
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