in a Purdue University study of agricultural chemical dealers were applying fertilizer by using controller-driven VRT (Akridge and Whipker, 1996).
Grid soil sampling also appears to be among the more popular precision agriculture technologies. The same Purdue University study found that 29 percent of the agricultural chemical dealers responding to the survey were pulling grid samples for their customers, whereas 15 percent were mapping fields (Akridge and Whipker, 1996).
Computers are a central component of information-intensive agriculture. Fewer than 10 percent of the 1.9 million producers and ranchers in the United States own computers. Commercial yield monitors are available for corn, soybean, and wheat harvesting. Approximately 2,000 yield monitors (cumulative) were in use in the 1995 growing season. This figure increased to about 9,000 in 1996. Midwest corn and soybean growers are currently the major buyers (A. Meyers, Ag. Leader Technology, personal communication, June 13, 1997).
There is a large body of economic and sociological literature on technology adoption in general, as well as on information-intensive agricultural technologies such as computers, integrated pest management (IPM), low-volume irrigation systems (drip, center-pivot, and other sprinkler systems), and the California Irrigation Management Information System (CIMIS), which combines localized weather information with field-level soil moisture monitoring to improve irrigation management. The history of the diffusion of these earlier information-intensive technologies offers insights into the likely prospects for current precision agriculture technologies.
It has long been recognized that new technologies diffuse gradually. Technological diffusion typically follows an S-shaped path over time. In the early years after a new technology is introduced, it is generally used by only a small percentage of those who could benefit from using it. As time passes, the rate of adoption tends to increase and diffusion becomes more rapid. Finally, after the majority of those who stand to benefit from using the technology have begun using it, the rate of diffusion slows again.
It is important to distinguish between two key variables characterizing the diffusion process: the extent (or ceiling) of adoption and the rate of adoption. The adoption ceiling pertains to the long term, when the diffusion process approaches completion. The rate of adoption pertains to the short term, while the diffusion process is in progress. Griliches's (1957) work on hybrid corn established that both the adoption rate and ceiling are influenced by economic factors. The adoption ceiling is influenced almost entirely by economic factors. In the short term, however, the rate of adoption is influenced by factors such as learning, risk and risk preferences, information, and human capital as well as by profitability considerations.