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The following study investigates the adoption of various precision farming technologies in terms of both the probability and the use intensity of technology components implemented. Zero-inflated Poisson and Negative Binomial count data model regressions were used to determine factors influencing farmers' decision to adopt greater number of precision technologies. Results from the count data analysis of a random sample of Ohio farm operators demonstrate that several factors were significantly associated with the adoption intensity and probability of precision farming technologies, including farm size, farmer demographics, soil quality, urban influences, farmer status of indebtedness, and location of the farm within the state.

Farmers are continuing to expand their use of computers. This article reports analyses for a 2003 random sample of Ohio, USA farmers. Computer adoption by Ohio farmers currently stands at 44 percent, up from 32 percent in 1991. Financial accounting remains the most often used task of farm computers, with 89 percent of farmers reporting such usage. However, the use of the Internet for communication, for transactions processing, or for information retrieval more broadly, is an application that was largely unavailable in 1991, and which is now used by about 80 percent of the farmers sampled. A binomial Probit model was formulated to determine those farm and farmer characteristics that influence farmers' decisions to adopt an office computer. Likelihood of computer adoption was found to increase with increased farm size (annual sales) and higher operator education. Younger farmers were significantly more like to adopt the computer. Adoption percent was higher on farms that were more reliant on leased land, and was lower for livestock farmers. Farmers who worked year-around away from the farm were more likely to adopt a computer for the farm business. A second binomial Probit model was formulated to identify those farmer characteristics associated with higher manager evaluations of the computer as a management tool. Likelihood of above average computer usefulness evaluations rose with increased farm annual gross sales, diminished with increased age of the operator, and were higher for farmers who used the computer for financial or production recordkeeping or who gathered information from the Internet.

Precision farming (PF) has the potential to help farmers improve input allocation decisions, thereby lowering production costs or increasing outputs, and, potentially, increasing profits. However, little is known about how farmers use PF technologies to support managerial decision-making, or about the relative magnitude of benefits and costs of PF technologies on individual farms. An embedded, multiple-case study approach was used to collect information about PF from six farms. The objective was to collect information about adoption and use of PF from early adopters to glean information that would be useful to those considering adoption of this farming system. Results suggest that farmers credit benefits to PF for a wide variety of decision types. The case study farmers appear to derive more value from information gathering technologies (e.g. yield monitors and mapping) than from variable rate application technologies.