Strategic management

Crop production is largely dependent on the characteristics of the soil in which they are grown. Farmers, since the advent of the tractor in the 1920s, have managed farms with a whole-field approach. Traditional farming (or the whole-field approach) requires farmers to apply inputs at the same rate across fields, regardless of the inherent variation in the soil and landscape. For example, farmers apply the same amount of fertilizer throughout the fields, regardless that some of the areas need more, or less, fertilizer. Precision agriculture tools offer various functions. Some are information gathering tools, such as yield monitors, targeted soil sampling, and remote sensing tools. Other tools are variable rate technologies that vary the rate of fertilizer, seeds, and pesticides. Additionally, guidance systems, such as light bars and auto steer equipment, help the operator guide the equipment.

Since the incorporation of precision agriculture tools began in the mid-1980s, initial adoption has been slow. Adoption of precision agriculture is progressing, though. A 2003 farm survey indicated that 32 percent of Ohio farmers has adopted at least one precision agriculture tool. While there seems to be a plethora of agronomic and economic research related to precision agriculture, the social sciences have been slow in analyzing the adoption and use of precision agriculture. Little is known about why farmers decide to (or not to) adopt these technologies. The purpose of this study is to create a model that describes, explains, and predicts farmers how make decisions whether or not to adopt precision agriculture technologies.

Precision Agriculture Technologies

Precision agriculture tools are used to monitor crop yields, to apply inputs at a variable, rather than at a constant rate, and to guide equipment. These tools are used to determine soil electrical conductivity, manage soil on a site-specific basis, and to monitor crop growth and health from satellite or aerial images. All of these tools use GIS to acquire, process, analyze, and transform the data that farmers can use to better manage production and increase profitability. GPS units are used to guide equipment during chemical and irrigation applications and during harvest.

Precision agriculture technologies are used for different purposes, and in various combinations, to fit the needs of individual producers. The information gathering tools, such as yield monitors, targeted soil sampling, and remote sensing, provide information about the fields as they vary in soil chemistry, moisture, fertility, topography, and productivity (yields). This information is entered into GISs that map these varying characteristics. The farmer uses GISs to create management zones which identify subsets of the fields that hold different soil properties and production potential. Farmers enter the appropriate rates of the inputs (i.e., fertilizer) for each management zone into the GIS. The management zone mapping from the GIS is then incorporated into variable rate applicators so the inputs are applied appropriately as the equipment passes through the fields.

Yield Monitors

        One information gathering tool is the yield monitor. Yield monitors are devices installed on crop harvesting equipment, such as combines or cotton pickers. Yield monitors collect information about the yield of the crop as the equipment travels through the fields. Yield monitors are the most widely adopted precision agriculture

tool. Yield monitors use GPS, GIS, computer, and sensor technologies to accurately measure the amount of crop harvested and moisture content of the crop at a specific location and time. Data gathered from yield monitors are transformed and transferred into the GISs so farmers can create detailed harvest reports, determine trends from harvest to harvest, compare the production capabilities of different varieties and crop inputs, and create management zones.

Targeted Soil Sampling

The soil type and its physical and chemical characteristics must be in proper balance to maximize production potential. Targeted soil sampling is a method to determine the chemical characteristics of the soil (i.e., acidity levels and nitrogen levels). Targeted soil sampling consists of two primary methods, grid and zone sampling. In each method, GIS software is used with the GPS to create a boundary of a field and divide the areas within the boundary into individual segments of grids or zones. Grids are normally square in shape and range in size from one-half to two and one-half acres in size. Zones are generally not uniform in shape, or size, and are often based on Natural Resource Conservation Service (NRCS) soil maps, areas of similar yield production, or any variable used for delineation. GISs provide the capability to collect and view soil sampling data. By using targeted soil sampling, farmers collect site-specific information that is used to make decisions on how to vary inputs in the management zones.

Remote Sensing

      Remote sensing, another information gathering tool, provides aerial and satellite images of the crop during its growing season. Remotely sensed data transferred to a GIS reveals information about soil characteristics, such as moisture content and general crop health. Remotely sensed data gives farmers near real-time information regarding their crop which allows them to make corrective management decisions by rectifying deficiencies before the crop is ready for harvest. This remotely sensed data, entered in a GIS, is used to make decisions on varying the rates of inputs in the management zones. This information is then transferred to variable rate applicators which apply the inputs as specific in the GIS.

Variable Rate Applicators

Variable rate applicators allow farmers to vary inputs, such as fertilizers, pesticides, seed varieties, and seeding rates throughout fields based on data retrieved from the information gathering tools. The input rates for management zones are entered into the GIS. This information is then transferred to the GPS-controlled variable applicator which is attached to the equipment. The purpose of varying input rates is to increase yields or reduce costs, depending on the managers' goal for the management zones.

Equipment Guidance Systems

Equipment operators have traditionally relied on visual cues, such as points on the horizon, marking systems consisting of foam emitters, tire tracks, or by counting number of rows to begin the next application pass. These methods lack the accuracy needed to avoid skips and overlaps. Additionally, they do not work at night. Equipment guidance systems, placed on agricultural machinery, are used to assist in steering the equipment in a more concise pattern by integrating GIS, GPS, on-board computing, and directional indicator devices to keep the machinery traveling in the most efficient manner across a field.

Auto steer equipment and light bars are two of the most widely used guidance tools. The lightbar uses a directional indicator device that provides navigational information to the operator. The auto-steer systems, similar to lightbar systems, actually steer the machinery, instead of the equipment operator. Auto-steer systems use a real-time kinematic form of GPS that incorporates a base station located on the farm that sends GPS data to the antenna located on the equipment. These guidance systems reduce redundancy, reduce labor costs, and expand hours of operation.

While some researchers have found that precision agriculture tools are adopted sequentially, others have found that the full potential of the individual components will not be realized unless the components are used as a set. For instance, the information captured with the yield monitor must be referenced and stored in a GIS. Next, maps are created and analyzed. Then, a variable rate applicator is used to vary chemicals throughout the fields according to the potential production of the grid or management zones.

Precision Agriculture Research

     The rapid growth of precision agriculture has sparked research in many areas to include agronomic evaluation of these technologies, development of appropriate uses of the technologies, demographic patterns of use of these technologies, and economic and environmental benefits of the technologies. The demographic research has focused on farm size, farming experience, education, access to information, location of the farm, and physical attributes of the farm, such as variability of soil types and crops grown. Most economic research in precision agriculture has focused on the profitability of specific tools in specific commodities.

Very little attention has been given to the perceptions and attitudinal reasons for farmers to adopt these technologies. Evaluating the perceptions and attitudes of farmers can lead to understanding why farmers adopt technologies beyond the economic benefit, and what industry and researchers may focus on to affect adoption of these technologies. Furthermore, the omission of producers' attitudes toward the technologies studied may lead to biased results.

A few studies have examined producers' attitudes toward precision agriculture used focus groups to identify several barriers to adopting precision agriculture. Two of these barriers were concerns over the initial cost of the technologies and keeping up with technologies that are rapidly changing. Napier et al. (2000) investigated producers' perceptions of the importance of conservation practices and having environmental information for management purposes and their intentions for using precision agriculture. They also investigated the farmers' perceptions of their ability in using precision agriculture. Napier et al. (2000) found that farmers who perceived that they would receive returns on conservation investments and that conservation information was important in farm management decision-making were more likely to adopt precision agriculture. The farmers' perceptions of their ability to use precision agriculture were not a significant factor in the intention to adopt precision agriculture.

Conversely, Adrian et al. (2005) found that farmers' confidence in using precision agriculture affected the intention to adopt of precision agriculture technologies. They also found that the farmers' perceptions of net benefit affected the intention to use precision agriculture technologies. The perceptions of ease of use were not a significant factor affecting the intention to adopt precision agriculture.

An Interdisciplinary Approach to Studying the Precision Agriculture Adoption

Decision Process

Precision agriculture technologies are used to manage specific areas of fields and to achieve long-term goals of sustainability by providing historical information on the soil and crop variations throughout farmers' fields. Precision agriculture also provides accurate recordkeeping for government regulations. Some researchers see precision agriculture as part of the larger context of Information Systems (IS) in agriculture. Just as IS is not a homogenous product, neither is precision agriculture. Rather, precision agriculture is the use of the various combinations of tools used for strategic, tactical, and operational improvement of agriculture production. While the potential of creating efficiencies exists with precision agricultural tools, these tools are fundamentally changing the way farmers make production decisions. Precision agriculture is an integration of GIS and GPS tools that can be used in a variety of combinations that fit the goals and operations of the farm manager. The steep learning curve of these technologies and the initial investment of each of the tools complicate farmers' adoption decisions. Part of the difficulty of researching these factors is that precision agriculture is not one particular tool, such as the motorized tractor, or a particular practice, such as no till farming. Farmers must decide which tools and software will provide efficiencies for their situations.

The IS field offers many methodologies for investigating technology diffusion, the intention to adopt information technologies, and attitudes and perceptions toward these technologies. Many of these methodologies borrow from the psychology, sociology, and organizational change theories. The IS field has established several streams of research that could be used in studying precision agriculture adoption, assimilation, and use.

One theory borrowed from psychology is the Theory of Reason Action (TRA) which defines attitudes toward a technology as the individual's beliefs about the consequences of adopting and using the technology and the assessment of these consequences (Fishbein & Ajzen, 1975). Attitudes toward a technology, particularly individuals' perceptions of their own capabilities and beliefs they can learn to use technology, affect whether individuals will adopt the technology. Further, attitudes of confidence in producers' abilities, have not been studied in the adoption and use of precision agriculture technology.

From sociology, Diffusion of Innovation (Dol) indicates that perceptions of relative advantage, complexity, and compatibility with values and operations will affect adoption of technology. Additionally, exposure to technology and interpersonal communication also affect the intention to adopt technologies.

Additionally, psychology offers other methods of explaining and predicting behavioral change and technology adoption. The Transtheoretical Model (TTM), developed in the 1980s predicts and explains behavioral change based on stages that one moves through as one is considering a change, then prepares for the change, makes the change, and maintains the changed behavior. The TTM incorporates how individuals value the advantages and disadvantages of behavioral change. Although the TTM has not been used in IS research, the model could be useful in analyzing the behavior change associated with adoption of technologies. The TTM is used as the basis of this study, along with TRA and Dol.

Summary

         Adoption of technology is not easily predicted solely on its potential economical benefits. Other factors affect farmers' decisions to adopt new technology. Utilizing the IS field is a logical choice in researching precision agriculture adoption because precision agriculture technologies are IS, mostly geographical in context, that are used to make management decisions and operate more efficiently. Additionally, other disciplines offer methodologies for studying the precision agriculture adoption-decision process. The IS field has borrowed from psychology and sociology disciplines in creating models that explain the use of information technologies.

Although the TTM has never been used in the IS field, it offers a process that could be useful in explaining the precision agriculture adoption decision. This study develops a model using the TTM and the stage of change as its focal point. Other theories from organizational change, sociology, and psychology are also used in developing this model to explain the precision agriculture adoption-decision process.