Genetic engineering is a technique used to alter or move genetic material (genes) of living cells. (A number of the terms used in this article are defined in Agricultural Biotechnology Concepts and Definitions). U.S. acreage using genetically engineered crops has increased from about 8 million acres in 1996 to more than 67 million acres in 1998, in major states where data have been collected. Has adoption of this technology benefited farmers and the environment?

Answering this question is not easy, even though survey data have been collected on the characteristics and performance of farms adopting biotech crops. Attributing differences in yields, pesticide use, and profits between adopters and nonadopters observed in the data solely to adoption of genetically engineered crops is nearly impossible because many other factors also affect yield and pesticide use. For example, producers with more favorable soils and climate may have higher yields than those operating under less favorable conditions, whether they used herbicide-tolerant varieties or not. Producers in areas of greater pest pressure may use more pesticide applications than those with fewer pest problems, despite adopting Bt crops.

However, the impacts of GMO (Genetically Modified Organisms) adoption can be explored by statistically controlling for other factors that also affect the impact. Multivariate regression modeling in effect decomposes the influence various factors exert on the decision to adopt GMO technology, and the influence of other factors on yields, pesticide use, and variable profits. The report Genetically Engineered Crops for Pest Management in U.S. Agriculture summarizes preliminary findings from such models using 1997 survey data.

Factors affecting adoption

What combination of producer characteristics and resource conditions are associated with greater probability of adopting GMO technology? Variables examined included farm size, operator education and experience, target pest for insecticide use, seed price, debt-to-assets ratio, use of marketing or production contracts, irrigation, crop price, and use of consultants. The statistical significance and importance of these variables varied among crops and technologies, illustrated by the cases of herbicide-resistant soybeans and Bt cotton.

Herbicide-resistant soybeans. Larger operations and more educated operators are more likely to use herbicide-tolerant soybean seed. As economists have observed in other cases, expected profitability positively influences the adoption of agricultural innovations. Thus, average crop price is a statistically significant and positive factor influencing adoption. Use of conventional tillage is another significant factor that reduces adoption, since farmers use conventional tillage to help control weeds, while herbicides are used with conservation or no-till practices. Weed infestation levels and seed price were positively correlated with adoption, with adopters preferring more expensive, higher quality seed, even excluding technical fees for herbicide-resistant varieties.

Bt Cotton. Adoption of insect-resistant cotton was modeled only in the Southeast (AL, GA, NC, SC) because insecticide use in this region was less affected by routine spraying regimes unrelated to the use of Bt cotton, such as boll weevil control in other producing regions, notably Mississippi. Production and marketing contracts and seed price were statistically significant variables positively associated with the adoption of Bt cotton. Presence of insect pests targeted for insecticide use was also statistically significant, but negative: more target pests treated with traditional synthetic insecticides are associated with lower Bt cotton adoption levels.

Modeling impacts of adoption

Given a specific level of GMO adoption, the impact can be assessed by controlling for the many factors that also contribute to that impact, in addition to using GMO seeds. Herbicide-tolerant soybeans and cotton and Bt-enhanced cotton crops are modeled individually. In each model, pest infestation levels, other pest management practices, crop rotations, and tillage are controlled for statistically. Geographic location is included as a proxy for soil, climate, and agricultural practice differences that might influence impacts of adoption. In addition, the impact model includes correction factors (obtained from the adoption model) to control for self-selection of the technology due to differences in producer characteristics between adopters and nonadopters.

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 and more producers adopt the technology, or in terms of a typical farm as they use the technology on more and 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.

Impacts from adopting herbicide-tolerant crops

Cotton production relies heavily upon herbicides to control weeds, often requiring applications of two or more herbicides at planting and post-emergence herbicides later in the season. Close to 28 million pounds of herbicides were applied to 97 percent of the 13 million acres devoted to upland cotton production in the 12 major states in 1997. In 1997, increases in adoption of herbicide-tolerant cotton are estimated to have increased yields, leading to increased variable profits (see table, Impact of Adoption of Herbicide-Tolerant and Insect-Resistant Crops pattern-text). However, no statistically significant change in herbicide use on cotton was observed in 1997.

By contrast, increased use of herbicide-tolerant soybeans (17 percent of 1997 soybean acres) produced only a small increase in yield, and no significant change in variable profits in 1997. Soybean production in the United States uses a large amount of herbicides, and 97 percent of the 66.2 million acres devoted to soybean production in the 19 major states were treated with more than 78 million pounds of herbicides in 1997. Genetic engineering produces tolerance to glyphosate herbicide in soybeans, of which 15 million pounds were used in 1997. However, almost two-thirds of the herbicides used on soybeans were other synthetic materials. As GMO adoption increased, use of glyphosate herbicide (such as Roundup©) also increased, but use of other synthetic herbicides decreased by a larger amount. The net result was a decrease in the overall pounds of herbicide applied.

Impacts from adopting insect-resistant cotton

Cotton production uses a large amount of insecticides, and 77 percent of the 13 million acres devoted to upland cotton production in the 12 major states were treated with 18 million pounds of insecticides in 1997. Malathion was the top insecticide used on cotton, with farmers applying more than 7 million pounds of this chemical in 1997. Aldicarb was second (2.4 million pounds), followed by methyl parathion (2 million pounds), and acephate (0.9 million pounds).

In 1997, an increase in adoption of Bt cotton in the Southeast (to 22 percent of cotton acres) led to an increase in cotton yields and variable profits (see table, Impact of Adoption of Herbicide-Tolerant and Insect-Resistant Crops). While use of organophosphate insecticides and pyrethroid insecticides did not have significant changes associated with an increase in Bt adoption, there was a significant decrease in other insecticides, such as aldicarb.

Impacts on pesticide use

Herbicide-resistant and Bt varieties appeal to producers because they promise to simplify pest management and reduce pesticide use, while helping to control costs, enhance effectiveness of pesticides and increase flexibility in field operations.

Reducing pesticide use through genetic engineering could also appeal to consumers. A Farm Bureau/Phillip Morris poll of farmers and consumers in August 1999, for example, indicates that 73 percent of consumers were willing to accept genetic engineering as a means of reducing chemical pesticides used in food production, and 68 percent considered farm chemicals entering ground and surface water to be a major problem.

The question remains: does adopting genetically engineered (GE) crops for pest management reduce use of chemical pesticides? To offer several perspectives on estimating changes in pesticide use associated with adoption of GE crops, ERS researchers analyzed this question using three statistical methods (see "Genetically Engineered Crops: Has Adoption Reduced Pesticide Use?").

• Same-year differences. Compares mean pesticide use between adopters and non-adopters 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.

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