The purpose of this descriptive-correlation study was to examine the variables associated with Northern Utah farmers’ adoption of auto-guidance technologies in alfalfa and corn silage production and determine training preferences. Participants in this study engaged in an experiential training session utilizing an auto-guidance system comparable to those available for use on their own farm. A survey was administered to identify autoguidance technology adoption and farmers’ preferences for related training. The majority of participants reported being male (f = 56, 98.2%). Half of the participants in this study (50.8%) indicated using auto-guidance technology in some form in their farming practices. Most attendees used auto-guidance technology with tractors (36.1%) and self-propelled windrowers (32.8%). Agricultural equipment businesses and Extension agents should help non-users to embrace new technology by using implementation statistics that include peer usage and management benefits.

Master of Agribusiness / Department of Agricultural Economics / Gregory Ibendahl / One of the latest buzzes amongst agriculture is the storage and analysis of “Big Data.” There are a number of questions surrounding the quality, quantity, and capacity of big data to form real-world decisions based upon past information. Much like the teachings of history, the storybook that big data can reveal about a grower’s operation may hold the answers to the question of: “what is necessary to increase food production which will be required to feed an ever-growing world?” With the increase in interest in precision agriculture, sustainability practices, and the processing of the immense spatial dataset generated on the farm, the next challenge at hand will be in determining how to make technology not only streamlined, but also profitable. Over the past few years, precision agriculture technology has become widely adopted as an agronomic decision making tool. Much like a scientific experiment, the greater the number of similar observations, the greater the degree of confidence can be placed upon a decision. As a means of increasing the number of observations that a farmer can use to base a decision upon, there is becoming increasing demand in being able to combine the data of similar farming operations in order to increase the size and scope of the dataset to generate better decisions benefitting many farms instead of just one. The growing interest in forming community data pools for farm data demonstrates the need for a study for determining how farming practices can be properly benchmarked. The goal was be to evaluate how to use farm data to make economic decisions in a similar manner as one would make agronomic decisions using similar observations. The objective was to design the proper protocol for benchmarking the farm’s potential, and evaluating potential increases in technical efficiency by adopting precision agriculture technology. To accomplish this, a data envelopment analysis was conducted using scale efficiency as a means of determining the frontier of efficient farms. The resounding goal for this study in the future will be to use the model as a means of implementing the secondary process of pooling precision agriculture data to analyze efficiencies gained by the adoption of technology. By demonstrating the value of generating peer groups to increase observations and refine farming practices, farmers can find increased profitability and efficiency by using resources that may already be held within the operation.