No Genetic Engineering and no Genetically Modified Organisms

4
Farm-System Dynamics and Social Impacts of Genetic Engineering

The dissemination of genetically engineered (GE) crops, like the adoption process associated with other farm-level technologies, is a dynamic process that both affects and is affected by the social networks that farmers have with each other, with other actors in the commodity chain, and with the broader community in which farm households reside. As noted in Chapter 1, farmer decisions to adopt a technology are influenced not only by human-capital factors, such as the educational level of the adopter, but by social-capital factors, such as access to information provided by other farmers through social networks (Kaup, 2008). That necessarily implies that farmers receive information from others—for example, on the risks and benefits of a particular technology—and that they share their own knowledge and experience through the same networks. Such findings confirm the relevance of social factors in influencing how genetic-engineering technology is adopted, what the impacts of its adoption are, and the significance of farmers’ active participation in both formal and informal social networks with other actors in commodity chains and communities.

However, little research has been conducted on the social impacts of the adoption of genetic-engineering technology by farmers, even though there is substantial evidence that technological developments in agriculture affect social structures and relationships (Van Es et al., 1988; Buttel et al., 1990). Because further innovations through genetic engineering are anticipated, such research is needed to inform seed developers, policy makers, and farmers about potential favorable benefits for adopters and non-adopters and unwanted or potentially unforeseen social effects (Guehlstorf, 2008). With such information, the likelihood of maximizing social benefits while minimizing social costs is increased. To demonstrate the necessity for increasing commitments to the conducts of research on the social effects of GE-crop adoption, this chapter synthesizes what is known in the scientific literature about the social impacts of farm-technology adoption and the interactions between farmers’ social networks. The chapter also identifies future research needs.

The earliest academic research in the United States on the social impacts of technology adoption at the farm and community levels was focused on mechanical technologies. More than a century ago, the use of machines in U.S. agriculture not only displaced labor but widened socioeconomic discrepancies between skilled and unskilled laborers (Quaintance, 1984). Academic interest in the socioeconomic consequences of agricultural mechanization was particularly strong in the 1930s and 1940s in the southern United States (Buttel et al., 1990) and again in the 1970s throughout the country. Berardi (1981) summarized the findings of the literature and found that mechanization was associated with decreases in the agricultural labor force, particularly those among the least educated and least skilled workers and in minority groups; with better working conditions and less “drudgery” for the remaining work force; with a decrease in farm numbers and an increase in farm size; with increased capital costs for agricultural producers; and with a decline in the socioeconomic viability of agriculture-dependent rural communities. Data also suggested that the technological development of U.S. agriculture had contributed to declines in farm labor, in community dependence on agriculture, and in rural community viability although other on-farm and off-farm factors also contributed to these changes (Van Es et al., 1988).

In the 1980s, social scientists broadened their research on the impacts of technology adoption on farms and farm communities to include studies of the potential and actual impacts of biological (pre genetic engineering) technologies in agriculture. Many observers assumed that, unlike the earlier wave of mechanical agricultural technologies, genetic-engineering technology would not be biased towards large-scale farming operations. Such an assumption was supported by analyses of the production capabilities of agricultural biotechnology. For example, it was noted that no interaction effect was observed between genetic predisposition to produce milk and the use of the growth hormone bovine somatotropin (BST) to increase milk production in dairy cows (Nytes et al., 1990). However, other studies that directly examined farm-level social change revealed that, despite the presumption of scale-neutrality, it was difficult to isolate the impacts of biological innovations from those of other technological innovations in agriculture because biological innovations were often developed and disseminated in conjunction with other technologies that may not have been scale-neutral (Kloppenburg, 1984).

Additional research conducted on the social impacts of biotechnology in animal agriculture, specifically on the use of BST, noted that rates of adoption of BST were moderate and that, although adoption did not require large herds, scale effects were observed because BST use was more effective in high-producing cows, which were more likely to be found in large herds with complementary feeding technologies (Barham et al., 2004). Beck and Gong (1994) also observed the existence of a scale effect with adoption of BST, with adopters more likely to have larger herds, as well as being younger and having more formal education. Additionally, it was suggested that the quality of farm management had an impact on the benefits accruing to the adoption of BST (Bauman, 1992). The use of BST also was thought to lead to lower prices and thus to result in increased economic pressure on smaller producers (Marion and Wills, 1990). In other words, the body of research on the socioeconomic consequences of the use of biotechnologies, including Green Revolution technologies, indicated that “scale neutrality is not inevitable, but a possibility that depends on institutional context” (DuPuis and Geisler, 1988: 410). To put it another way, the social context of the adoption process and the impacts on that context are interconnected, from which it follows that the social impacts of genetic-engineering technology on farms and communities differ among cultures, commodities, and historical periods.

Thus, though seed varieties are generally conceptualized as being scale-neutral, the adoption of any technology may be biased toward large firms that can spread the fixed costs of learning over greater quantities of production (Caswell et al., 1994). In developing countries, the economics of genetic-engineering technology do not appear to vary with farm size (Thirtle et al., 2003). However, scale may affect accessibility to technology. Small farmers have less influence in input supply and marketing chains with which to secure access to desired technologies. Thus, there can be a scale bias in the development and dissemination processes associated with herbicide-resistance technology that puts small farmers at a disadvantage. In contrast, as noted in Chapter 3, insect-resistance technology can replace insecticide applications that require fixed capital investments, such as for tractors and sprayers. In this regard genetic-engineering technology has the potential to favor small farmers, who would benefit more from a technology that required less fixed capital investment. The scale effects of transgenic varieties may also depend on the pricing (such as quantity discounts) set by seed companies, which typically assess a technology-user fee.¹ 

An early empirical study was carried out by Fernandez-Cornejo et al. (2001) using 1998 U.S. farm data. They found that, as expected, the adoption of HR soybean was invariant to size, but adoption of HR corn was positively related to size. They explained this disparity as due to the different adoption rates: 34 percent of the farms had adopted HR soybean at the time, implying that adoption of HR soybean had progressed past innovator and early adopter stages into the realm where adopting farmers are much like the majority of farmers. On the other hand, adoption of HR corn was quite low at the time (5 percent of farms), implying that adoption was largely confined to innovators and other early adopters who in general tend to control substantial resources and who are willing to take the risks associated with trying new ideas. Thus, they claimed that the impact of farm size on adoption is highest at the very early stages of the diffusion of an innovation (HR corn), and becomes less important as diffusion increases. This result confirms Rogers’s (2003) observations that adoption is more responsive to farm size at the innovator stage, and the effect of farm size in adoption generally diminishes as diffusion progresses. Early adopters, by virtue of early adoption, also are able to capture a greater percentage of the economic benefits of the technology adoption process.

Clearly, one cannot extrapolate the social impacts of the adoption of GE crops based solely on an assumption that the productive capabilities of genetic-engineering technology, when isolated from the interaction with other factors, should be scale-neutral. In other words, previous research on the social impacts of agricultural technologies suggests the possibility that the early dissemination of genetic-engineering technology would be associated with farm size, and that the use of GE crops could have differential impacts across farm types, farm size, and region, despite the fact that GE crops are presumed to be scale-neutral.

In an article that attempted to predict some of the environmental, economic, and social effects of genetic engineering of crops, it was argued that the use of GE crops was “clearly capable of causing major ecological, economic, and social changes” (Pimentel et al., 1989: 611). Nonetheless, over the last decade, there has been virtually no empirical research conducted on the social impacts of the use of GE crops on farms and rural communities. The lack of research may have to do in part with the scarcity of funds available for such research as well as a relative lack of interest in social issues on the part of environmental groups (Chen and Buttel, 2000), and other groups and organizations that might be expected to support such research. Nonetheless, the results of research referred to above on the social repercussions of agricultural technologies, including non–genetic-engineering biotechnology in crops and biotechnology in animal agriculture, would suggest that there are impacts, that these impacts could be favorable or adverse, and that adverse impacts could be alleviated through the adoption of appropriate policies. For example, based on earlier research on the introduction of new technologies in agriculture, it might be hypothesized that certain categories of farmers (those with less access to credit, those with fewer social connections to university and private sector researchers, etc.) might be less able to access or benefit from existing GE crops. There is also the possibility that the types of genetic advances being marketed do not meet the needs of certain classes of farmers, and that the full spectrum of the potential of genetic-engineering technology is not being achieved. Furthermore, the possibility exists that communities where farmers play an important social, political, and economic role could be impacted as well. However, for the purpose of this report, no conclusion on the social impacts of the adoption of GE crops can be drawn on the basis of empirical evidence. Research on such impacts clearly should be accorded a high priority as genetic-engineering technology evolves. Without such research, the potential for genetic-engineering technology to contribute to the sustainable development of U.S. agriculture and rural communities cannot be adequately assessed. Thus, we recommend that such research be sponsored and pursued actively and immediately.