Precision Agriculture Technology Approaches

“There are four (4) key areas in Precision Agriculture Technology: (1) location determination in outdoor areas through the use of the Global Positioning System (GPS), (2) Computerized geographic Information Systems (GIS), (3) Automated application of crop inputs (VRA), and (4) data collection and automated mapping via sensors (Zhang, et al., 2002, Pedersen & Lind, 2017). These systems should enable farmers “to increase yield, save nutrients and replace labour time with efficient sensing and decision-support systems that can increase profitability on the farm and reduce the negative environmental impact”. (Pedersen & Lind, 2017)

Adoption is defined as the continued use of new technology by a farmer over time (Rogers, 1983).

Global studies show an unsteady trend of Precision Agriculture Adoption (Norton & Swinton, 2001). These differences in adoption vary by region, by countries within the same region and also by industries and individual farmers. Lowenberg-DeBoer (1999) reported high adoption of yield monitors in Argentina, but reduced adoption in Brazil and France. A study by Daberkow and McBride (2000) in the United States showed only an 11.3% adoption of Precision Agriculture Technology on farms in Midwestern part of the country and only 1.1% adoption in the Southeastern Seaboard region of the United States. Evidence in these studies suggest countries such as the United States, Canada, and Australia have the highest adoption rate of at least one Precision Agriculture Technology on 5-15% of their crops (Swinton & Lowenberg-DeBoer, 2001)

The decision to adopt Precision Agriculture Technology is multifaceted. Earlier research on the adoption decision, first noted by Beale and Bolen (1955) focused on awareness. They argued that being aware of the existence of the technology is very important in a famer’s decision to use it. Later studies focused more the extent and rate of adoption (Hiebert, 1974, Nelson & Phelps, 1966, Welch, 1970), and the factors influencing the decision to adopt (Feder, 1980, Just, et al., 1983). More recent studies concentrate on evaluating the factors influencing why farmers decide to adopt or not adopt Precision Agriculture Technology (Tey & Brindal, 2012).

Various factors have been identified that affect adoption. Some studies have classified the factors into broad categories for analysis. These categories range from farm and farmer characteristics (McNamara, et al., 1991) to social, economic and physical categories (Kebede, et al., 1990) and informational and ecological (Nowak, 1987). Tey & Brindal (2012) devised seven categories for Precision Agriculture Technology adoption as: (1) socio-economic, (2) agro-ecological, (3) institutional, (4) informational, (5) farmer perception, (6) behavioural, and (7) technological factors. While there has been extensive research on the drivers influencing the adoption of Precision Agriculture Technology, there are areas that have been ignored or not given adequate attention. For instance, Tey & Brindal failed to include Technology Acceptance Model (TAM) in their analysis, thus eliminating some key factors that affect behaviour and intention to use technology.

Conducting this literature review is to set the ground work of what has already been identified by previous research as factors influencing the adoption of Precision Agriculture Technology and to merge those factors for the purpose of this research. It also seeks to identify gaps in the literature which the study seeks to fill. The research is presented focusing on the adaptation Technology Acceptance Model (TAM) and Opinion Leadership.

This literature review includes the empirical literature on the topic, theoretical frameworks developed from past studies, and the problems perceived in those studies. It also explains the research framework adapted for this study. The empirical research yielded many results of differing models used to analyze the adoption of Precision Agriculture Technology. Though the topic was the similar, they were either out-dated or did not match the interest of this research. However, they were not discarded as other information was beneficial to writing this research.

Adoption of improved agricultural technologies and practices have come to the forefront in discussion of increased productivity of safe food and environmental preservation and protection. The decision to adopt a technology is quite intricate for a farmer. There have been many studies identifying factors of whether to adopt or not adopt agriculture technology. Farmers not only decide if to adopt a technology, but also what rate and for what period of time. There are many factors that influence the adoption decision, including land tenure, access to credit and farmers attitude to risk (Anderson & Thampapillai, 1990). Sall et al. (2000) found that farm and farmer characteristics with farmers’ perceptions of technology played a vital role in the decision process. De Souza Filho et al. (1999), reported affiliations with other organizations, such as non-governmental organizations (NGOs) affected the adoption decision. Likewise, Thapa and Rasul (2011) identified motivation by both Governmental and non-government organizations and community members determined whether farmers chose to adopt Precision Agriculture Technology. A study by Adrian et al. (2005) also reported attitude towards agriculture technology and the perceived net benefits influenced the adoption.

In 2013, Pierpaoli et al., wrote a comprehensive literature review of the drivers of Precision Agriculture Technologies adoption. The study was divided into two groups of research: ex-post and ex-ante assessments. Ex-post refers to evaluating farmers who had adopted Precision Agriculture Technology and ex-ante evaluated farmers before the actual decision to adopt or not. There was a total of thirteen (13) Ex-Post papers reviewed in the study. Those papers were published between 1998 and 2012. The methods utilized in those papers were mostly Logit, and a few Probit, Tobit and cross tabulation analysis. There were seven (7) Ex-Ante papers reviewed in the study, ranging from 2002 to 2012. The methods used included TAM, SEM, Probit and factorial design. Pierpaoli et al., outlined three (3) main drivers connected to intention to adopt Precision Agriculture Technology as: financial resources, socio-demographic factors and competitive and contingent factors. In the Ex-Ante papers, Pierpaoli et al., described the factors that could affect the decision to adopt Precision Agriculture Technology. With the use of TAM, Pierpaoli et al., suggests that previous authors failed to include important factors. In TAM, one of the constructs Pierpaoli et al., found was significant in determining to adopt was Perceived Usefulness. This is defined as “degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989). Pierpaoli et al., describes these constructs as factors affecting attitude to adopt as seen in Figure
2.

Antolini et al., (2015) further reviewed the literature from Souza Filho et al. (2011), Tey and Brindal (2012) and Pierpaoli et al. (2013) stating that all three studies are valuable to the literature because they integrate the principal elements that govern the adoption of Precision Agriculture Technology. Antolini et al. (2015) also did a systematic literature review inclusive of 36 empirical studies from various countries. The authors found most studies followed the ex-post approach and used Logit and Probit methods. The dependent variable in the empirical studies was noted as Precision Agriculture Adoption. Based on the seven categories encapsulated from the empirical studies, Antolini et al. (2015) proposed an integrated framework for adoption of Precision Agriculture Technology by farmers. They incorporated the identified factors with TAM 3 (Venkatesh and Bala, 2008) to devise the proposed framework. TAM 3 is the evolution of previous versions of TAM, which is a model developed to explain how individuals come to accept and use technology.

Thoroughly explaining the complexities of human behaviour has proven to be a difficult task. Still many researchers have taken on the challenge in trying to dissect human behaviour and the reasons for their actions. One such scholar is Icek Ajken, who developed the Theory of Planned Behaviour (TPB) in 1985. This theory is an extension of the Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1980).

The model is based on the premise that individuals make reasonable and logical decision towards certain behaviour based on the information available to them in that situation. Figure 3 illustrates the Theory of Planned Behaviour. The theory is designed to link one’s beliefs with actual behaviour. TAM is one the most influential extensions of TRA and TPB developed by Davis in 1989. TAM itself has been further developed over the years with TAM2 and TAM3. This research will focus on TAM which enhanced TPB by adding the Ease of Use and Usefulness constructs to the model. The Ex-Post drivers of adoption demonstrated by Pierpaoli et al. (2013) serve as an illustration of the framework developed using TAM. The Ex-Ante factors in Figure 5 are also of interest to this study. The integration of both ex-post and ex-ante approaches gives a holistic overview of factors that influence Precision Agriculture Adoption.

The two approaches are both based on three categories: socio-demographic factors, competitive and contingent factors, and financial resources. Socio-demographic factors refer to the personal background information of the farmer. The literature notes age, years of education and years of experience as significant in this area. Previous studies reveal age has a negative relationship with adoption of Precision Agriculture Technology, while higher levels of education and experience have a positive relationship. The older a farmer is the less likely he will be motivated to change his processes and have less exposure to technology (Roberts et al., 2004). The more educated and experienced farmers are more willing to take risks if it returns greater profitability (Feder, 1982). Competitiveness and Contingent factors are the variables that allow or promote the adoption of Precision Agriculture Technology. Financial Resources factors refer to financial risk in relation to the perceived financial rewards in deciding to adopt Precision Agriculture Technology.”

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