The widespread adoption of genetically engineered (GE) crops has clearly led to changes in pesticide use, but the nature and extent of these impacts remain open questions. We study this issue with a unique, large, and representative sample of plot-level choices made by U.S. maize and soybean farmers from 1998 to 2011. On average, adopters of GE glyphosate-tolerant (GT) soybeans used 28% (0.30 kg/ha) more herbicide than nonadopters, adopters of GT maize used 1.2% (0.03 kg/ha) less herbicide than nonadopters, and adopters of GE insect-resistant (IR) maize used 11.2% (0.013 kg/ha) less insecticide than nonadopters. When pesticides are weighted by the environmental impact quotient, however, we find that (relative to nonadopters) GE adopters used about the same amount of soybean herbicides, 9.8% less of maize herbicides, and 10.4% less of maize insecticides. In addition, the results indicate that the difference in pesticide use between GE and non-GE adopters has changed significantly over time. For both soybean and maize, GT adopters used increasingly more herbicides relative to nonadopters, whereas adopters of IR maize used increasingly less insecticides. The estimated pattern of change in herbicide use over time is consistent with the emergence of glyphosate weed resistance.

Keywords

Our analysis relies on a unique, large farm-level data set that spans the period 1998–2011. The data have been assembled annually by GfK Kynetec, a unit of a major market research organization that specializes in the collection of agriculture-related survey data. For each year, the samples are designed to be representative at the crop reporting district (CRD) level and include an annual average of 5424 farmers for maize and 5029 farmers for soybeans (table S1). On the basis of these data, for each farmer, we match the amount of pesticide used with the size of the corresponding plot and the attributes of the seed planted on that plot (including the type of GE traits embedded). Some farmers make more than one chemical/seed choice in any 1 year (that is, they have more than one plot), and some (but not all) are observed for more than 1 year (fig. S1). Thus, we can estimate the impact of GE crops on pesticide use by means of a fixed-effects regression analysis with observations on a large number of individual plot-level choices.

The prevailing consensus is that IR crops have significantly reduced insecticide use, but for herbicides, the literature is divided ( 11 , 12 ). Because most studies have lacked extensive survey data ( 11 ), a key issue has been how to impute counterfactual herbicide use for GE adopters. Some have used rates based on recommended conventional herbicide programs ( 13 – 15 ). However, these recommended rates are much larger than the average observed herbicide usage rates before the advent of GE crops ( 9 , 10 ), so that unsurprisingly, this method suggests large reductions in herbicide use due to GE adoption. Studies that instead rely on observed herbicide usage rates have hitherto been limited to 1 or 2 years of data, and in the earlier stages of GE crop adoption ( 16 – 18 ). Hence, the generality of their results is limited, and they cannot shed light on whether the impact of GE variety adoption on pesticide use has changed over time. In particular, there have been little data to assess whether the recent development of glyphosate-resistant weeds has eroded whatever herbicide use benefits there may have been from GT crops ( 11 ).

One of the most salient developments in global agriculture in the past 20 years has been the introduction of genetically engineered (GE) crop varieties ( 1 – 5 ). In the United States in 2015, GE varieties accounted for 94% of planted soybean and 93% of planted maize ( 6 ). Adoption of this new technology was rapid: First introduced in 1996, GE soybean varieties embedding the glyphosate-tolerant (GT) trait have exceeded 80% of planted hectares since 2003. The share of planted maize using GE varieties—embedding GT and/or insect-resistant (IR) traits—has exceeded 80% since 2008. GT varieties are complementary inputs with glyphosate, and their adoption has inevitably led to substitution away from other herbicides ( 7 ). Conversely, IR varieties can substitute for the use of insecticides, conceivably leading to lower pesticide use. Because pesticides have implications for human health and ecological diversity, factors that affect their use are of considerable policy interest ( 8 – 10 ). However, the nature and extent of the impact of GE variety adoption on pesticide use remain open questions.

RESULTS

Data on pesticide use and GE crop adoption in U.S. soybeans and maize are shown in Fig. 1. For maize, the share of varieties containing the GT trait (whether alone or stacked with IR traits) is reported separately from the share of varieties embedding one or more IR traits (henceforth Bt maize) (Fig. 1A). The rate of use of insecticides applied to maize fell from 0.2 kg/ha in 1998 to about 0.05 kg/ha in 2011, a 75% decrease (Fig. 1B). Since 1998, the most striking trend has been an increase in the use of glyphosate (Fig. 1, C and D). By 2011, glyphosate dominated the soybean herbicide market with just over 80% of total herbicide applied, and in maize, it accounted for nearly 40% of applied herbicide (a near 20-fold increase from 1998). Increased glyphosate use came at the expense of other herbicides, although for soybeans there was also an increase in total herbicide use that began in 2007 and steadily rose through 2011.

Fig. 1 GE variety adoption and pesticide use, maize and soybeans in the United States, 1998–2011. (A) Adoption rates of GT soybeans, GT maize, and Bt maize (embedding one or more genes from Bacillus thuringiensis). (B) Insecticide use in maize (kg/ha and EIQ weights). (C) Herbicide use in soybeans (kg/ha and EIQ weights). (D) Herbicide use in maize (kg/ha and EIQ weights). Adoption rates and active ingredient (a.i.) use (kg/ha) are reported in tables S12 and S13.

The average rates in Fig. 1 are constructed by adding the amount of active ingredients of a large number of different chemicals. A concern with this (common) procedure is that the total weight associated with a bundle of heterogeneous chemicals is a poor measure of environmental impact (19, 20). There is no agreed-upon superior procedure to aggregate heterogeneous pesticides. Following other studies (13, 14, 21), we use the environmental impact quotient (EIQ) (22) as an alternative benchmark. Specifically, each active ingredient is weighted by its EIQ value (23), and the resulting weighted sum is normalized so as to have the same overall mean as the unweighted total. Despite certain shortcomings (24), the EIQ’s appeal in our context is that it converts an array of attributes specific to each pesticide into a single value meant to summarize the toxicity of the chemical. In general, reweighting chemicals by their EIQ score does not significantly affect overall trends in pesticide use, except for soybeans where, from 1998 to 2005, the herbicide rate slightly increased but declined in the EIQ-weighted amount (Fig. 1).

To further investigate the impact of GE variety adoption on pesticide use, we use our plot-level data to estimate the fixed-effects regression model outlined in Materials and Methods. We consider two different measures of the amount of pesticides per unit of land applied by growers: unweighted sum of all active ingredients used (kg/ha) and EIQ-weighted sum. The model is estimated separately for soybean herbicides, maize herbicides, and maize insecticides. For soybeans, we have a total of 86,736 plot-level observations, whereas for maize we have a total of 134,264 observations.

To assess the average impact over the entire 1998–2011 period, we first estimate the fixed-effects model under the restriction that the impact of GE varieties is constant over time, that is, β t = β, ∀t (Table 1; full results in table S2). Overall, GT soybeans increased the quantity of herbicides used by 0.30 kg/ha (a 28% increase relative to the average use by non-GT growers over the entire period). When herbicides are weighted by their EIQ score, however, the coefficient of the adoption variable is not significantly different from zero, reflecting the relatively lower EIQ values for glyphosate. For maize, GT adopters used about 0.03 kg/ha less herbicide (a 1.2% decline relative to the average overall use by non-GT growers). In EIQ terms, the savings were larger at 9.8%, again reflecting the relatively low EIQ values for glyphosate. With respect to insecticides, GE adopters of IR varieties used about 0.013 kg/ha less insecticide than nonadopters (an 11.2% decline relative to the average overall use by non-Bt adopters), a difference that is essentially unaffected by EIQ weighting.

Table 1 Estimated impact of GE varieties on pesticide use, average impact over 1998–2011 (assumes β t = β, ∀ t). N = number of observations. SEs (in parentheses) are clustered at the farmer level. The model includes time fixed effects, CRD-specific time trends, and individual (farmer) fixed effects. *P < 0.05, **P < 0.01, ***P < 0.001. a.i., active ingredient. View this table:

The EIQ index is composed of three subcomponents: farmworker EIQ, which accounts for farmer exposure to dermal and chronic toxicity; consumer EIQ, which captures exposure to chronic toxicity and potential groundwater effects; and ecology EIQ, which captures the impacts of chemicals on fish, birds, bees, and beneficial arthropods (22). To gain further insight into the EIQ result in Table 1, we decompose the G i coefficient into these three subcomponents. For all soybean herbicides, corn herbicides, and maize insecticides, the farmworker and consumer components were lower on account of GE variety adoption. For the ecology component, maize herbicides and insecticides were improved by GE adoption, but for soybean herbicides, GE adoption had a detrimental effect (Table 2). Because leaching potential and dermal toxicity are specific to the farmworker and consumer components, these results are broadly consistent with previous work that finds that herbicide usage patterns associated with GE varieties are beneficial (16, 18).

Table 2 Estimated impact of GE varieties on the farmer, consumer, and ecology components of EIQ-weighted pesticide use, average impact over 1998–2011 (assumes β t = β, ∀ t). N = number of observations. SEs (in parentheses) are clustered at the farmer level. The model includes time fixed effects, CRD-specific time trends, and individual (farmer) fixed effects. *P < 0.05, **P < 0.01, ***P < 0.001. View this table:

Next, we estimate the model where the β t parameters are allowed to vary over time. The full results are reported in table S3; here, we graph the estimated β t coefficients, along with their 95% confidence interval (Fig. 2). The impact of GT variety adoption on herbicide use has changed markedly over time. In all periods, GT soybean adopters used more herbicide than nonadopters, and this difference increased considerably over time. By 2011, the amount applied by GT adopters was 0.66 kg/ha greater than nonadopters, an increase of 0.49 kg/ha from 1998. Moreover, although the total amount applied by a GT user was initially less harmful (as measured by the EIQ), from 2003 onward, the reverse applied. The estimated trend for the impact of GT adoption for maize herbicides shows a similar pattern: Over time, GT adopters gradually used more herbicide relative to conventional users, and by 2008, this difference was positive and statistically significantly greater than zero. Even when weighted by the EIQ impact, by 2011, GT adopters used more herbicide per hectare than nonadopters.

Fig. 2 Estimated β t parameters from the fixed-effects model. (A) Year-specific impacts of GT soybeans on herbicide use (kg/ha and EIQ weights). (B) Year-specific impacts of GT maize on herbicide use (kg/ha and EIQ weights). (C) Year-specific impacts of Bt maize on insecticide use (kg/ha and EIQ weights). For all panels, vertical bars denote 95% confidence intervals.

As for the impact of GE maize varieties embedding Bt traits, GE adopters used less insecticide than conventional growers for all years since 2000 (Fig. 2). The reduction in insecticide use attributable to the adoption of GE varieties increases (in absolute value) and becomes more significant (statistically) over time, possibly because of the diffusion of GE maize varieties with multiple Bt traits (for example, conveying resistance to corn rootworm, in addition to the European corn borer). In interpreting these results, however, one should bear in mind the possibility that Bt adoption might reduce the need for insecticide use by nonadopters as well, via an area-wide suppression effect, a conjecture supported by some evidence (25, 26).

Whereas Fig. 2 illustrates the estimated differential pesticide use by GE adopters relative to nonadopters, it is also of interest to investigate the underlying time trend of pesticide use by nonadopters. This information is conveyed by the year-specific intercepts of the estimated model. Figure 3 graphs the estimated α t coefficients (full results are in table S3). For maize herbicides, there was a steady downward trend in herbicide use per hectare. Much of this downward trend can be explained by the decline of certain high-rate herbicides. For example, the active ingredient metolachlor was supplanted by the lower-rate S-metolachlor, and cyanazine was phased out by the U.S. Food and Drug Administration (in cooperation with DuPont) by 2002 (fig. S2). Other low-rate herbicides, such as mesotrione, also gained market penetration over the study period. For soybean herbicides, a downward trend also occurred early on, but the trend inverted in 2006. For maize insecticides, the use by nonadopters declined steadily up to 2007, stabilizing thereafter. This is broadly consistent with stylized facts concerning insecticide use in U.S. agriculture (8–10). More specifically, even before the introduction of Bt crops, there was a trend toward products with lower application rates. Neonicotinoids, which are applied in the form of seed treatments, are one class of low-rate insecticides that have been widely adopted recently. By 2011, our data indicate that nearly 50% of applied weight in insecticides took the form of seed treatments (Fig. 1B).

Fig. 3 Estimated α t parameters from the fixed-effects model. (A) Year-specific herbicide use by non-GT soybean adopters (kg/ha and EIQ weights). (B) Year-specific herbicide use by non-GT maize adopters (kg/ha and EIQ weights). (C) Year-specific insecticide use by non-Bt maize adopters (kg/ha and EIQ weights). For all panels, vertical bars denote 95% confidence intervals.

The robustness of the results obtained from the baseline model was investigated by considering several variations: the alternative where farmers’ heterogeneity is instead represented by a random-effect model (table S4), explicit accounting for the expansion of no-tillage practices (table S5), explicit representation of plot-specific weed pressure (table S6), accounting for selection bias due to the possible role of unobserved plot-level heterogeneity (tables S7 and S8), and omission of choices associated with zero pesticide use (tables S9 and S10). Details for each of these variations, and an additional discussion, are provided in the Supplementary Materials. Overall, the results of interest are essentially unchanged under these alternative specifications.

A clear result that emerges from our analysis is the change in differential herbicide use by GT adopters relative to non-GT adopters over time. What are the sources of such significant and persistent upward trends? Explanations such as the expansion of no-tillage practices or unobserved plot-level heterogeneity can be ruled out on the basis of the alternative specifications noted above. Part of the trend can be explained simply by the fact that nonadopters, particularly in maize, transitioned to lower-rate herbicides, but this cannot explain the sharp increase in later periods (specifically, 2007–2011). One explanation not ruled out by our investigations concerns the possible role of weed resistance. This is of particular interest, as glyphosate weed resistance has recently emerged as a significant concern (27–29). With GT crops, growers can apply glyphosate multiple times in a relatively short time span. Furthermore, the simultaneous availability of GT soybeans and GT maize has led to maize-soybean rotations that use glyphosate exclusively, thus significantly reducing the degree of chemical heterogeneity faced by weed populations (an important factor for preventing the emergence of herbicide tolerance) (29).

Making a direct link between our results and weed resistance is difficult because the data do not contain a plot-level variable that correlates with glyphosate weed resistance. To pursue an indirect inference route, however, we decompose the results in Fig. 2 by estimating the fixed-effects regressions separately for glyphosate and nonglyphosate herbicides. The underlying rationale for this procedure is that one of the early indicators of resistance would be a relative increase in the use of nonglyphosate herbicides by GE adopters. We find that for both soybeans and maize there has been a significant increase in nonglyphosate herbicides applied by GT adopters (relative to non-GT users) (table S11). In soybeans, a GT adopter in 1998 used about 0.71 kg/ha less in nonglyphosate herbicides relative to a conventional user; by 2011, the difference was just 0.48 kg/ha (Fig. 4A). In maize, GT adopters went from using 1.31 kg/ha less in nonglyphosate herbicides in 1998 to only 0.32 kg/ha less in 2011 (Fig. 4B). The role of glyphosate weed resistance is also supported by data on the fraction of GT plots that relied exclusively on glyphosate for weed control. Up to 2006, more than 70% of land planted with GT soybeans and more than 40% of land planted with GT maize were treated exclusively with glyphosate. However, since then, these rates have dropped significantly, reaching lows of 41% (soybeans) and 19% (maize) (Fig. 4C).