Agriculture is a critical sector for all countries because it feeds their populations. The planet has experienced a consistently rising population for decades, which means that there is an ever-growing demand for food. Therefore, the agricultural sector has had to undergo constant development in terms of the use of labor, land, and biological resources. One of the most recent developments involves digital technologies. According to Akmarov, Gorbyshina, and Kniazeva (2019), innovations in the agro-industrial complex comprise modern information technologies, production robotization, and automation. Additionally, it is important to acknowledge that digitization serves two purposes: increasing productivity and minimizing losses (Kotsur, Veselova, Dubrovskiy, Moskvin, & Yusova, 2019). Despite the capability of digital technologies to help farmers achieves this objective, not all have embraced the new technologies, which means that their production capacities remain limited.
There may be skepticism surrounding new technologies among the farmers, but this can only be assumed to be one of the explanations for the reluctance. There are dangers for failing to adopt digital tools, some of which include inadequate food production for a growing population. Therefore, this study seeks to explore the dynamics of digital technology by farmers over the past two decades, focusing specifically on the perspectives and perceptions of the farmers. The aims and objectives of this study are listed below:
- To explore the country’s political goodwill and its effect on technology adoption.
- To establish how digital technologies solve the problem of growing demand.
- To explore the socio-cultural factors that impact the ability of the farmers to embrace digital technologies.
Recipients of the Research
As a student, the primary recipients of this research are my academic institution and my research supervisor. Therefore, the thesis follows all academic guidelines provided by the institution and is accomplished through support and direction from the supervisor. However, it is important to acknowledge that the study explores real-life issues that affect our countries and the planet as a whole. Therefore, the outcomes of the research may be of particular interest to the company and industry personnel in the agricultural sector. These stakeholders are keen to understand what digital technologies can do for their performance and profits. Most importantly, the farmers can learn critical lessons regarding the benefits of digital technologies and can help them embrace them.
Suitability of the researcher for the Research
As a researcher, my suitability for this research emanated from the courses taken and the fact that agriculture is a major subject in business. Additionally, information technology (IT) is a subject that has become common among scholars across all fields. In business disciplines, IT is another tool for conducting business and a mechanism for offering greater operational efficiencies. Having studied IT in my course as one of the many subjects means that I can conduct scholarly research on the subject.
Literature Review
The subject of digital transformation has been studied extensively with various scholars focusing on different aspects. The underlying theories of production and technology have been used to illustrate how digital technologies improve output. Specific theoretical frameworks give production as a function of inputs and outputs, for which technology is seen as a key determinant. It is currently observable that the key focus for many previous studies has been on how these technologies influence agricultural productivity. However, specific technologies have also been explored, as well as their operational outlook. For example, geographical information system (GIS) has been studied by Akmarov, Gorbyshina, and Kniazeva (2019), who explain that digital technologies enhance adaptive-landscape approaches through planning and effective use of land resources. GIS has been deployed in such countries as Russia to conduct critical assessment activities, including the suitability of land, visual analysis, demarcate ecological and economic zones, and running data on rural settlements. Therefore, it can be seen that digital technologies have proven critical in agricultural planning.
Digital agriculture also involved considerable use of IT in the collection, storage, and analysis of huge amounts of data. Therefore, some scholars have also paid significant attention to the concept of big data and how it revolutionizes agriculture. According to (Sarker, Islam, Ali, Islam, Salam, and Mahmud, 2019), big data can be used to enhance sustainable farming, which means ensuring that demand is met within the available resources. In essence, digital agriculture tends to generate massive amounts of specific information regarding the status of fish, soil, crop, animals, and even intercultural management requirements. This data is used for effective farm management, even though some farmers may have a negative attitude towards implementing big data technologies. Further observations include that big data and analytics help create strong relationships between the agricultural stakeholders, including farmers, policymakers, academicians, administrators, and practitioners. Due to the ability to provide adequate and processed information, big data is critical for agricultural planning, especially among the farmers, input suppliers, business people, and machinery producers. These stakeholders are in the frontline of agricultural production, and their activities and output may depend on adequate, efficient planning.
The importance of big data in digital agriculture can be manifested by the number of studies exploring the subject. A study by Himesh, et al. (2018) expresses that big data is at the heart of the digital revolution in agriculture, especially since the emergence of the internet of things (IoTs), sensor technologies, and mobile data. These researchers explain that the digital transformation in the agricultural sector is characterized by new information and communication technologies (ICTs) and IoTs that are taking place at a rapid pace. These technologies are deployed in the collection, analysis, and archival of data for decision-making in the agricultural sector. Additionally, farm machinery is now fitted with sensors that collect far-level data, for instance, regarding crop and soil. The processes information is used by the farmers to make such decisions as irrigation and crop choice. Examples of the digital technologies given by Himesh, et al. (2018) include the Integrated Field System that collects on-site data on weed, soil, and weather. Additionally, the Agro-Climate Impact Reporter is used for climate and weather information deployed at a large scale across such countries as Canada.
While the studies on big data explain which technologies and how they are used, they have failed to express the trends among the farmers and the rate of adoption for specific countries. This is a major research gap even in other studies exploring specific technologies. For example, enterprise resource planning (ERP) has been discussed by Kutnjak, Pihil, and Tomicic-Pupek (2020), where the focus has been on the benefits. The adoption by farmers and the factors underlying the trends remain sidelined despite the observation that some farmers remain skeptical. However, it is important to acknowledge that these studies have provided critical insights into the subject and should lay the foundation for the current research.
It is important to acknowledge that digital technologies go beyond the big data explained above. Other aspects of the digital transformation include the concept of Industry 4.0, which, in the agricultural context, is labeled Agriculture 4.0 (Ozdogan, Gacar, & Aktas, 2017). However, big data remains a critical part of Agriculture 4.0 because t is used alongside other such technologies as IoTs and even drones. The main idea behind Industry 4.0 is that there is a growing usage of smart technologies, which often involve artificial intelligence (AI) to aid in automation. Robotics is a critical component in automation, which is the key indicator of the degree of digital transformation.
According to Moysiadis, Tsolakis, Katikaridis, Sorensen, Pearson, and Bochtis (2020), the emergence of mobile robots in agriculture signifies the digital transformation where automation has been used to optimize the labor-intensive, time-consuming, and resource-demanding fields of agricultural operations. In agriculture, automation demands the use of advanced robotic systems in four key operational areas: localization, perception, planning, and execution. This literature highlights further developments and the benefits to be derived from the digital transformation.
The dynamics regarding the adoption of technologies by farmers are scantly explored, with only a few studies focusing on specific contexts, including Asia, Africa, and the developing countries. The position that the farm-level utilization of digital technologies remains inadequately studied has been backed by Miller, Griffin, Bergtold, Ciampitti, and Sharda (2017). The trends in implementation across different countries are affected by different factors. Additionally, different regions experience varied rates of adoption, but the contributing factors remain relatively ignored. Some of the technologies are novel, especially those encompassed in Industry 4.0. Therefore, the last two decades can be perceived to have been a transformation for the agricultural sector, which means that the trends in adoption levels and other dynamics are an interesting research topic.
Ethical Issues
Conducting primary research requires engaging human beings in the process, mostly as the source of information. Multiple ethical issues need to be addressed by a scholar, especially those that focus on the protection of participants. Therefore, the researcher will keep the respondents anonymous by not requesting or recording their personal details. Privacy will also be maintained in a similar manner because people are entitled to it. Additionally, signed informed consent would be required because all participation is voluntary. Lastly, minors will be excluded from the research for their own protection.
Research Methodology
This research requires the scholar to take ample time to produce an in-depth analysis of the problem and its possible solution. It might require a high level of reasoning, defined by Ju and Choi (2018) as the process of offering explanations for observational data through logical steps used to solve a problem and reach a decision. Hypothetico-deductive reasoning (HDR) is the proposed design for this study due to its effectiveness across multiple fields of social sciences. Therefore, hypotheses are formulated and tested through deductive research. The purpose of the hypotheses is to help produce a general theory that can be proved through the analysis of the collected data. The null and alternate hypotheses that will be tested are outlined below:
- H1o. Digital transformation is not reliant on the goodwill of a country’s political goodwill.
- H1a. Digital transformation is reliant on the goodwill of a country’s political goodwill.
- H2o. Digital transformation is not the solution to the growing demand for food at a time when climate change is negatively impacting the agricultural sector.
- H2a. Digital transformation is the solution to the growing demand for food at a time when climate change is negatively impacting the agricultural sector.
- H3o. Socio-cultural practices and beliefs have a direct impact on the ability of farmers in a given country to embrace smart farming technologies.
- H3a. Socio-cultural practices and beliefs have a direct impact on the ability of farmers in a given country to embrace smart farming technologies.
The deductive research will require massive amounts of data, which forces the scholar to use both primary and secondary data. Therefore, pragmatism is adopted, which makes it possible to select the research methods that best suit the study. Pragmatism is a methodological approach developed by such scholars as William James, Charles Sanders Pierce, and John Dewy to encompass feasible and workable solutions to complex problems (Parvaiz, Mufti, & Wahab, 2016). In research, pragmatism allows researchers to focus on the nature of the research itself as opposed to the methods.
Through pragmatism, the researcher has settled on a mixed-methods approach. Many scholars believe that not selecting a single qualitative or quantitative approach alone is the ideal paradigm (Sahin & Öztürk, 2019). Therefore, the mixed-method approach is increasingly used across many fields, including educational and social sciences. The mixed-methods approach allows the research to use different methods for data collection and analysis without necessarily mixing them. In this research, both primary and secondary data are collected and analyzed, which means different methods will be needed for each of them.
The primary data can be used to prove the hypothesis through deduction, which means a quantitative approach may be used. The survey research will require the researcher to develop a mechanism to quantify the primary data, for which the Linkert scale works best. For analysis, descriptive statistics help display the demographic characteristics of the respondents. A correlation analysis highlights how the variables are related, while a regression analysis shows the relationship between the independent and dependent variables.
The secondary data can be analyzed using thematic or content analysis. These methods work best with qualitative textual data, which can be collected from literature materials and other published works. Depending on the nature of the survey questions, primary textual data collected can also be subjected to these methods of data analysis. Thematic and content analysis both involve codes and coding, which are applied to the messages obtained from the research (Neuendorf, 2019). The research will use thematic analysis for the secondary data, where the themes will be built around the hypotheses. The outcomes of the hypothesis testing can be subjected to further discussion from the themes obtained from previous research.
Population and Sample
The collection of primary data will involve engaging human subjects from whom information is needed. In this case, the farmers are the individuals who implement and use digital technologies, and they will comprise the research respondents. A sample of 100 farmers will be selected using random sampling. The term sampling is used in research to refer to the device or procedure for selecting a smaller number of items to represent the population. The items are the subset of the population, which mostly involved people in social science research. One hundred farmers are selected to represent the entire population of farmers in the United Arab Emirates (UAE). With this sample, the data collected will be used to generalize across all farmers. Therefore, primary researchers are often concerned with the size of the simple because smaller samples can offer a biased view. However, 100 can be described as a representative sample because it is large enough to allow for unbiased generalization.
As mentioned earlier, a random sampling method is used in this research due to the merits it offers a researcher. First, random sampling is considered to be the easiest among all sampling techniques. Random sampling means a researcher spends less time and effort assembling a sample. Secondly, random sampling produces a representative sample since all subjects have equal chances of selection. Lastly, unbiased selection means the sample itself is unbiased, which makes the generalization of findings easier. Even with the constraints of random sampling, it is important to mention that there will be predetermined inclusion criteria to allow the researcher to collect the best sample possible. First, the farmers must be from the UAE, which is the country selected for the study. Secondly, all respondents must be English literate since the study is conducted in the English language. The use of the survey research means the respondents must be able to read and write. Lastly, all the respondents must be 20 years and above, which allows the scholar to exclude minors and those with little farming experience.
Data Collection, Editing, and Coding
Primary data is collected through survey research, where a questionnaire is administered to all the 100 respondents. Due to the ongoing coronavirus pandemic and the social contact regulations across the UAE, the questionnaires will be administered electronically, using either emails or social media. The researcher assumes that all farmers above the age of 20 years are fully aware of these technologies, especially social media. Additionally, the fact that the majority of the UAE adults own and operate a smartphone means that the farmers will be easily accessible. The questionnaires comprise questions built around the research questions focusing on the degree of adoption and the underlying factors to the implementation of digital agricultural technologies.
Secondary data is collected through a search of literature materials, mostly from online databases and repositories. Most universities today maintain online libraries or have access to such sources of data. The researcher will search the secondary data through the use of keywords, which will be developed to mirror the themes. The secondary data will be coded along with the themes, where texts are classified under a broad theme. Deductive coding means that a codebook will be developed before data collection, which can be done through the review of available literature.
Time, Cost, and Project Management
Completing this thesis maybe take a considerable amount of time, especially because of the collection and analysis of primary and the possibility of conducting a pilot study. Therefore, the researcher hopes to complete the study in less than a year. The major activity will be the collection and analysis of data, which should take up to four months. The remaining activities can be accomplished within five months, which means the entire timeframe spans nine months.
The costs and project management are other critical elements to consider for this undertaking. Firstly, the researcher will spend a significant amount of money to accomplish certain activities, including printing and subscriptions to the digital libraries. A comprehensive breakdown of the costs will be presented in Table 1 below. In terms of project management, the scheduling of major activities can also be presented in a tabular form. While the activities will not be different from the representation in the Gantt chart, the start and end dates are outlined in Table 2 to offer a more detailed view.
New Relevant research
The literature section has highlighted the current streams of research, indicating that most scholars have explored the uses and benefits of digital technologies. However, the underlying factors affecting the rates of adoption and the trends and other dynamics surrounding the implementation of digital technologies are areas that remain sidelined. This research explores these areas to fill the gap and offer new and relevant research. The rationale is that some farmers remain skeptical about new technologies, which has affected their decisions to use digital technology. This can be a serious issue at a time when the demand for food and other agricultural outputs is growing. Therefore, this research helps explore this phenomenon and offers a pathway for the development of potential solutions. Agriculture may not be the largest sector in the economy of the UAE. However, its development since the efforts of self-reliance began means agriculture is critical to the country. The relevance of this research extends to its applicability by farmers and their operations, as well as the country’s policymakers and industrialists.
References
Akmarov, P., Gorbyshina, N., & Kniazeva, O. (2019). Special aspects of digital transformation in agriculture sector of economy. Advances in Intelligent Systems Research, 167, 22-26.
Himesh, S., Rao, P., Gouda, K., Ramesh, K., Rakesh, V., Mohapatra, G.,… Ajilesh, P. (2018). Digital revolution and Big Data: A new revolution in agriculture. CAB Reviews, 13(21), 1-7.
Ju, H., & Choi, I. (2018). The role of argumentation in hypothetico-deductive reasoning during problem-based learning in medical education: A conceptual framework. Interdisciplinary Journal of Problem-Based Learning, 12(1), 1-16.
Kotsur, E., Veselova, M., Dubrovskiy, A., Moskvin, V., & Yusova, Y. (2019). GIS as a tool for creating a global geographic information platform for digital transformation of agriculture. Journal of Physics: Conference Series, 1399, 1-5.
Kutnjak, A., Pihil, I., & Tomicic-Pupek, K. (2020). Smart agriculture and ERP benefits in the context of digital transformation. Economic and Social Development: Book of Proceedings, 21-33.
Miller, N., Griffin, T., Bergtold, J., Ciampitti, I., & Sharda, A. (2017). Farmers’ adoption path of precision agriculture technology. Advances in Animal Biosciences: Precision Agriculture, 8(2), 708-712.
Moysiadis, V., Tsolakis, N., Katikaridis, D., Sørensen, C., Pearson, S., & Bochtis, D. (2020). Mobile robotics in agricultural operations: A narrative review on planning aspects. Applied Sciences, 10(10), 1-17.
Neuendorf, K. (2019). Content analysis and thematic analysis. In P. Brough, Advanced research methods for applied psychology (pp. 211-223). London: Routledge.
Ozdogan, B., Gacar, A., & Aktas, H. (2017). Digital agriculture practices in the context of Agriculture 4.0. Journal of Economics, Finance and Accounting, 4(2), 184-191.
Parvaiz, G., Mufti, O., & Wahab, M. (2016). Pragmatism for mixed method research at higher education level. Business & Economic Review, 8(2), 67-9.
Sahin, M., & Öztürk, G. (2019). Theoretical foundations, designs and its use in educational research. International Journal of Contemporary Educational Research, 6(2), 301-310.
Sarker, N., Islam, S., Ali, A., Islam, S., Salam, A., & Mahmud, H. (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies, 25(4), 1235-1240. Web.