The International Consortium on Agricultural Biotechnology Research (ICABR)


Adoption and Diffusion of rBST in California.

L.J. Butler
University of California of Davis,

Irene Henriques,
York University,

The problem of projecting future use patterns of agricultural biotechnology products in order to evaluate their potential for development and commercialization is a challenging proposition for economists. While many have attempted ex ante methods of projecting adoption and diffusion rates, very few have tested their ex ante results after the fact. In this paper, we use the results of a continuous survey of California dairy producers ex post to test the predictions of an ex ante study of adoption of recombinant bovine Somatotropin (rbST) carried out using data 4-7 years prior to the availability of the new technology.

In 1987, a survey of California dairy producers was carried out to determine their attitudes and concerns about technology adoption, and particularly about rBST (recombinant Bovine Somatotropin). A sample of 152 dairy producers (about seven percent of total) was drawn from a complete list of all Grade A producers in California. The same producers have been continuously surveyed each year since 1987. In 1990 the original survey sample was increased to represent approximately 10 percent of all California dairy producers. In 1994, after extensive debate and testing, rBST was finally approved for commercial use on US dairy farms. In 1997/98, in addition to surveying the entire panel of dairy producers, an extensive survey was made of almost 50 percent (1,000 producers) of the California dairy industry on adoption or non-use of rBST.

This study uses data collected from California dairy producers 4-5 years after the commercial release of rbST to test the validity of predictions made using data collected from the same source 4-7 years prior to the commercial availability of rbST. A relatively straightforward discriminant function analysis, using exactly the same variables as the ex ante model shows that our abilities to project adoption rates, at least in this case, are fairly accurate. Our relatively simple model shows that the variables selected prior to the commercial availability of rbST were indeed relatively accurate predictors of the actual adoption rate 4-5 years after the commercial release of the new technology.

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