A model for determination of economic thresholds, or minimum weed population densities justifying the use of postemergence herbicide treatment, for five weed species in soybeans [Glycine max (L.) Merr.] is presented. Sensitivity analysis was performed on the model with respect to economic, statistical, and agronomic variables. The model was refined to include uncertainty about lost field days during the spraying period. Predictions from both the simple and refined models were consistent with economic theory. It was also determined that the economic threshold is sensitive to choice of data-collection ranges and functional form in weed-interference studies.

Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.

This study identified the factors that influenced whether farmers in the Southeastern United States perceived an improvement in environmental quality from adopting precision farming technologies (PFTs). Farmers with larger farms or higher yields were more likely to believe that they observed positive externalities associated with PFTs. Farmers who found PFTs profitable or who believed input reduction was important had higher probabilities whereas those with higher incomes or who were more dependent on farm income were less likely to perceive such benefits. Interestingly, the importance of environmental quality and length of time using PFTs were not found to affect the probability of perceiving an improvement in environmental quality.

Ray et al. have examined the national agricultural sector impacts of the 1996 FAIR Act using a stochastic simulation model based on the Policy Analysis System (POLYSYS). The model outcomes are predictions of various economic measures, including the coefficient of variation of net returns for corn, wheat, soy-beans, and cotton. Then Knutson et al. use the results of the Ray et al. simulations to predict the distribution of net farm income over the next 10 years for several types of representative southern farms. Any attempt to measure the changes in riskiness in southern agriculture is commendable—even heroic—and I applaud these efforts, both the national modeling effort by Ray et al. and the application to southern representative farms by Knutson et al. This task has a lot in common, I think, with trying to “tease out” the temperature changes associated with the greenhouse effect. The number of factors and mechanisms at work is mind-boggling. It's tough enough to try to get a handle on what might happen to the first moment of net farm income over the next 10 years, let alone the second.

Probit analysis identified factors that influence the adoption of precision farming technologies by Southeastern cotton farmers. Younger, more educated farmers who operated larger farms and were optimistic about the future of precision farming were most likely to adopt site-specific information technology. The probability of adopting variable-rate input application technology was higher for younger farmers who operated larger farms, owned more of the land they farmed, were more informed about the costs and benefits of precision farming, and were optimistic about the future of precision farming. Computer use was not important, possibly because custom hiring shifts the burden of computer use to agribusiness firms.

Binary logit analysis was used to identify the factors influencing adoption of Global Positioning System (GPS) guidance systems by cotton farmers in 11 Mid-south and Southeastern states. Results indicate that adoption was more likely by those who had already adopted other precision-farming practices and had used computers for farm management. In addition, younger and more affluent farmers were more likely to adopt. Farmers with larger farms and with relatively high yields were also more likely to adopt. Education was not a significant factor in a farmer's decision to adopt GPS guidance systems.

This article investigates how information from cotton yield monitors influences the perceptions of within-field yield variability of cotton producers. Using yield distribution modeling techniques and survey data from cotton producers in 11 southeastern states, we find that cotton farmers who responded to the survey tend to underestimate within-field yield variability (by approximately 5-18%) when not using site-specific yield monitor information. Results further indicate that surveyed cotton farmers who responded to a specific question about yield monitors place a value of approximately $20/acre/year (on average) on the additional information about within-field yield variability that the yield monitor technology provides.

Personal digital assistants (PDA) and handheld global positioning systems (GPS) have become increasingly important in cotton production but little is known about their use. This research analyzed the adoption of PDA/handheld GPS devices in cotton production. A younger farmer who used a computer in farm management and had a positive perception of Extension had a greater likelihood of adopting the devices. In addition, farmers who used complementary remote sensing, plant mapping, and grid soil sampling information were more likely to use PDA/handheld GPS devices. Finally, the COTMAN in-field decision support program from Extension also positively impacted adoption.

This study examines the notion that fresh-fruit prices follow a time trend separate from the effect of seasonal supply changes over the marketing season. In particular, quality changes and a limited marketing season are hypothesized to contribute to the seasonal changes in demand. The empirical results support these hypotheses for the Boston fresh-blueberry market.

A model is developed to determine the minimum weed population where a decision to apply a postemergence herbicide would be profitable. The economic threshold model accounts for changing economic conditions, the effect of weeds on crop quality, the effect of multiple weed species on yield and quality, and uncertainty about spraying period length. The model is uncomplicated enough for microcomputer or programmable calculator applications. An example of weed threshold calculations for round white potatoes is given.