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Article

A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies

by
Anastasios Michailidis
1,*,
Chrysanthi Charatsari
1,
Thomas Bournaris
1,
Efstratios Loizou
2,
Aikaterini Paltaki
1,
Dimitra Lazaridou
3 and
Evagelos D. Lioutas
4,*
1
Department of Agricultural Economics, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Department of Regional and Cross Border Development, University of Western Macedonia, 50100 Kozani, Greece
3
Department of Forestry and Natural Environment Management, Agricultural University of Athens, 36100 Karpenisi, Greece
4
Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
*
Authors to whom correspondence should be addressed.
Agriculture 2024, 14(1), 99; https://doi.org/10.3390/agriculture14010099
Submission received: 4 December 2023 / Revised: 28 December 2023 / Accepted: 3 January 2024 / Published: 4 January 2024
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

:
The penetration of precision agriculture technologies in agrifood systems generates the need for efficient upskilling programs targeted at farmers and other actors. A critical first step in this direction is to uncover the training needs of the actors involved in precision agriculture ecosystems. The present study aimed to identify and assess gaps in competencies related to precision agriculture technologies of Greek livestock farmers and researchers specialized in this field. For farmers, we followed a partially mixed research design. To uncover researchers’ training needs, we chose a qualitative-dominant mixed approach. The results revealed that farmers lack competencies concerning the exploitation of precision agriculture technologies. Depending on their area of expertise, researchers have needs associated with predicting how research affects the future of farming and understanding how precision agriculture artifacts interplay with socio-environmental and economic factors. Despite the limited generalizability of the findings, which represent a limitation associated with the reliance of data on two small sample sizes, our results indicate that, beyond technology-related competencies, it is essential to enhance the capacity of producers and researchers to foresight and shape potential (digital) futures.

1. Introduction

Precision agriculture—initially called “precision farming” [1] or “high-precision agriculture” [2]—emerged as a term to describe an array of practices involving soil sampling, quantities and timing of inputs, fertilizer and agrochemicals selection, and choices about the machinery used to manage cultivations. After almost three decades of life, precision agriculture still lacks a commonly accepted definition. By combining published work in the field [3,4,5,6,7,8,9,10], in this study, we conceptualize precision agriculture as a form of agriculture that, by exploiting temporal data collected with the use of sophisticated technologies, using algorithms to uncover relationships between ecological, meteorological, and biological variables, and utilizing high-tech farm machinery, can improve farm management decisions and reduce uncertainty, thus increasing the efficiency, productivity, profit, and sustainability of farm enterprises while promising to enhance the environmental performance of agriculture.
Precision agriculture technologies include global positioning systems (GPS), geographic information systems (GIS), remote sensing, decision support systems (DSS), variable-rate technologies, wireless sensor networks, tractor guidance and driver assistance systems, herd management software and animal tracking systems, and blockchain technology [11,12,13,14,15,16,17,18,19,20]. The use of these technological advancements can increase the economic performance of farms [10,21] by improving management decisions [22], reducing labor costs and pesticides [23], and increasing profit margins [24]. However, adopters of precision agriculture are exposed to higher economic risks [25] due to the high levels of financial investment needed to buy relevant technologies [26].
As Pierce and Nowak [27] argue, the success of precision agriculture depends on adopters’ capacity to apply its technologies and techniques. In this vein, skills and competencies are essential resources for farmers to reach the full potential of precision agriculture [5,28]. These competencies comprise managerial [29] and technical capabilities [30], data and information interpretation capacity [31,32,33], and skills in integrating technologies into farm routines [34] and efficiently using them while farming [35,36,37]. Nevertheless, beyond pure technical competencies, social and interpersonal skills are equally important [38].
Although farmers’ skills have received considerable attention from scholars so far, little is known about researchers’ training needs in precision agriculture. Shepherd et al. [39] note that researchers should build both technological capabilities and skills in implementing their scientific knowledge to facilitate the exploitation of these technologies in farm practice. However, while research on precision agricultural technology development and application abounds, some studies suggest that researchers are not always well equipped with the skills needed to understand (potential and actual) adopters’ priorities and problems [40]. On the other hand, being industry-driven, precision agriculture technology development often targets wealthy, technology-intensive, and more efficient industries, like dairy farming [41]. Hence, researchers may lack competencies in understanding what precision agriculture can offer to other sectors of farming activity—especially in alternative farm production practices, such as agroecology [42] or organic farming [43]—and how. Another issue deserving investigation is whether researchers are competent in promoting a societally responsible introduction of innovations [44] and understanding their own responsibility in the process of innovating [45]. Conducting responsible research means possessing skills in managing threats that precision agriculture technologies open up [46] and evaluating the positive and negative impacts of these technologies across different levels [47].
In the present study, we aim to offer a preliminary view of the precision agriculture-related skills and competencies of Greek farmers and researchers. To do so, we examine farmers’ competencies in technology-related domains and areas pertaining to skills assisting adopters in navigating the digital transition. We also attempt to uncover the competency needs of researchers studying precision agriculture. From a methodological standpoint, adopting a social science lens, we combined quantitative and qualitative techniques to identify and assess the competency needs of farmers and researchers.
The rest of the article is organized as follows. In the next section, we detail the methods used in our study. Then, we present the results and discuss our main findings. In the final section, we present a synopsis of the conclusions and discuss the limitations of the study.

2. Methods

2.1. Participants and Research Design

For this study, we used data from a sample of eight Greek livestock farmers who recently adopted precision agriculture technologies and five researchers working in different institutes in the country. For researchers, we adopted a qualitative-dominant mixed research approach. To uncover farmers’ training needs, we followed a partially mixed concurrent equal status research design [48]. We used open- and closed-type questions to collect qualitative and quantitative data. Such a simultaneous “QUAL + QUAN” research design can lead to the collection of broader and more complete data than those gathered through monomethod approaches [49], offering, in parallel, the opportunity to explain phenomena and relationships that emerge through only qualitative or only quantitative paradigms [50].

2.2. Instruments Used

For assessing the skills of farmers and, consequently, identifying their training needs, we a priori divided competencies into two categories. The first one comprises “technology-related” competencies that concern the adoption, use, and exploitation of precision agriculture technologies [47,51]. The second category refers to transition-related competencies, which are associated with skills needed during the post-adoption phase to navigate the digital transition of farms.
For the first category, we generated nine items concerning competencies associated with selecting (e.g., “choosing appropriate technologies for my farm”), using (e.g., “properly using technologies”), and bundling technologies (e.g., “connecting precision agriculture technologies with traditionally used technologies”). For transition-related competencies, we developed 18 items, referring to technology exploitation (e.g., “using technologies in a way that maximizes the benefits for my farm”), integration competencies (e.g., “making technology a part of my enterprise”), skills related to ensuring the best fit between technologies and farms (e.g., “being able to make changes when technologies don’t fit the purposes of my farm enterprise”), and competencies needed to resolve human–technology conflicts (e.g., “facilitate the collaboration of human actors and technologies in my farm enterprise”). Following previous works on competencies assessment [52,53], we used a five-point scale ranging from 1 (not at all) to 5 (very much) to assess the degree to which farmers have each one of the competencies included in the two scales developed.
Moreover, we used a single item to rate farmers’ overall competency level in precision agriculture on a scale from 1 to 10. In addition, respondents were invited to answer a series of open-ended questions aiming to uncover how they are building skills related to precision agriculture.
To identify the training needs of researchers working in fields revolving around precision agriculture, we focused on five dimensions derived from the relevant literature [54,55,56,57,58,59,60], namely:
  • Understanding farmers’ problems and needs.
  • Assessing the compatibility of precision agriculture technologies to different types of farming.
  • Reflecting on the impacts of precision agriculture.
  • Visioning the future of farming and their roles in shaping it.
  • Promoting the responsible exploitation of precision agriculture technologies.
An interview guide was developed based on social science research in the field of digital agricultural technologies [44,57,58,59,60,61] to allow the collection of information on these dimensions. We also added some questions to help us understand how researchers conceive of precision agriculture and their role in its evolution and a self-assessment item concerning their overall competency levels in precision agriculture. Finally, we used a closed-type question in which participants were asked to rate their level of expertise in different areas related to precision agriculture on a scale ranging from 1 to 5.

2.3. Data Analysis Techniques

To analyze quantitative data, we used descriptive statistics (mean scores and standard deviations). For handling the qualitative data, we performed a conventional content analysis [62]. We first generated initial codes from the data. Then, we collated these codes to create overarching codes, which were combined to form themes. For the farmers’ sample, we arrived at two themes. The first one referred to the importance of technical knowledge and farm reorganization competencies after the adoption of precision agriculture technologies for shaping the identity of a “good farmer”. The second summarized codes concerning the (quantitatively and qualitatively) insufficient upskilling opportunities for farmers. In the case of researchers, the themes evolve around participants’ difficulties in understanding the cultural and normative aspects of farming, their capability to assess the fit between precision agriculture technologies and agroecosystem properties, and researchers’ limited capacity to fully embrace the complexity of the interrelations between technology and the social dimension of farming. In Figure 1, we present the code maps for the two datasets involved in our study.

3. Results

3.1. Farmers

For farmers, knowledge is considered one of the crucial factors leading to the success of farm enterprises. More than just helping them to improve productivity levels, they conceive knowledge as an essential component of a “good farmer’s identity.” As a participant noted, knowledge is what distinguishes good farmers from those who are not aware of the changes occurring in agriculture. Notably, in their answers, farmers stressed the need to enhance not only their technical skills but also their competencies in planning new business models for exploiting precision agriculture technologies.
When asked how a farmer can develop skills in precision agriculture, most interviewees stated that training can open new possibilities for self-development and success. Nevertheless, there is a lack of relevant agricultural education or training initiatives. In addition, the offered programs cannot deliver the expected results. According to respondents, the main reason behind the inefficiency of educational and training interventions is the limited emphasis of training programs on the farm practice and the mismatch between the content of the offered programs and the real problems that farmers face.
As most participants stated, although they prefer experiential educational/training programs, which can offer them the opportunity to learn by doing and create experiences that will help them construct new knowledge, the available seminars mainly concern lectures loosely connected with farmers’ experience and previous knowledge. Farmers also stress the need to foster the role of the Ministry of Agriculture in the organization of educational courses. To date, the ministry is involved in and organizes training programs that target “young farmers” (i.e., new entrants in the agricultural sector, aged below 40 years old) but does not provide training in modern issues, like precision agriculture. In addition, respondents expressed concerns about the skillset of trainers who work in the programs offered by the Greek state. In their view, trainers have a central role in educational activities. As a participant mentioned, they should “have the expertise to deliver the knowledge and motivate farmers to participate”.
Another issue that emerged during the analysis of qualitative data was the nonavailability of asynchronous learning opportunities, which could allow self-paced learning and give farmers access to a variety of digital didactic material. A digital platform could also facilitate farmers’ skills building since it would provide farmers with the appropriate knowledge and information on precision agriculture. Interviewees also stressed the need for training videos and digital material for competencies development. The Internet was mentioned by many participants as a popular channel through which they can share their experiences, problems, and ideas, hence co-creating knowledge.
Our quantitative analysis revealed that the lack of training programs is reflected in the overall level of farmers’ skills and knowledge of precision agriculture. The maximum score of respondents’ overall competency on the one-to-ten scale used was seven. Interestingly, the participant with the highest rate noted that he developed precision agriculture-related knowledge during his studies in a technological educational institute: “Through my studies on tertiary education, I have a quite high level of skills and knowledge on precision farming.” The lowest score was 2.0, showing that farmers lack relevant competencies, while the mean score of farmers’ self-ratings was lower than 4.0.
Farmers’ technology-related competencies had mean scores ranging from 3.63 to 4.63 on a five-point scale (Table 1). The lowest mean scores were observed for the items concerning the adoption decision (“Choosing appropriate technologies for my farm”), the attempts to match existing and new technologies (“Connecting precision agriculture technologies with traditionally used technologies”), and the difficulties and problems associated with the adopted technologies (“Solving problems associated with newly introduced technologies”). On the contrary, farmers seem to not face issues related to the allocation of on-farm activities after adopting precision agriculture technologies, as the high mean score of the item “Reorganizing work after technology adoption” indicates.
The mean scores for transition-related competencies ranged from 3.43 to 4.38. The lowest mean scores were observed for two items concerning the ability of farmers to facilitate farm workers’ engagement with precision agriculture technology, namely “Helping farm workers exploit technologies for the benefit of my farm” and “Understanding how farm workers feel about technologies and resolving potential conflicts.” Moreover, items referring to farmers’ competency in integrating precision agriculture technologies into their farm enterprises (“Facilitate the collaboration of human actors and technologies in my farm enterprise” and “Being capable of integrating these technologies into the way of doing business”) had mean scores of 3.88.
On the other hand, as Table 2 shows, respondents’ ability to learn either through interacting with technologies (item “Learning how to learn from technologies”) or through the practice of technology integration in the farm (item “Being able to learn while integrating technologies into my farm enterprise”) received the highest mean scores, revealing that farmers have sufficient learning integration competencies. Moreover, it is noteworthy that farmers have a notable capacity to control their emotions while interacting with precision agriculture technologies, as the mean score of the item “Handling my emotions when things go wrong” shows.

3.2. Researchers

Interviewed researchers self-rated their expertise in precision agriculture on a one-to-ten scale. One of the participants was unwilling to answer the question. The remaining four researchers gave evaluations ranging from 7 to 8. From their comments emerged the conclusion that they consider the lack of field experience and the multidisciplinary nature of precision agriculture (which creates the need to familiarize themselves with and develop knowledge of other scientific fields) to be factors that reduce researchers’ levels of expertise. Indeed, participants work in different sub-fields of precision agriculture research, including yield monitoring and prediction, data analysis, information systems, remote sensing with satellites and drones, and precision fertilization using artificial intelligence.
The analysis showed that most interviewees feel quite confident in their ability to estimate the effectiveness of different precision agriculture technologies under varying agroecological conditions. Their expertise in the specific area has been developed mainly during their involvement in relevant (cross-border) research activities. As an interviewee mentioned: “I have been involved in and coordinated several research projects and field experiments related to precision agriculture across the highly variable climatic zones of Europe.” However, a participant stated that he was “moderately” able to estimate the fit between technologies and agroecological conditions. In his view, such a capacity requires multidisciplinary skills that a single researcher can hardly develop. This remarkable opinion emphasizes the need to view researchers’ training through a multidisciplinary lens.
A notable finding was that, although interviewees declared that they are highly competent in finding compatible technologies for different types of farming, their responses were centered around soil conditions or the crops cultivated. On the contrary, we did not yield comments concerning different farming approaches (e.g., biodynamic farming, organic agriculture). Once again, it seems that researchers emphasize the technical features of precision agriculture implementation without paying equal attention to its cultural or normative aspects.
The same observation has been noticed in the answers concerning the interrelations between precision agriculture technologies and socio-economic factors. Although four out of five participants noted that they are able to understand how technologies affect and are affected by social, economic, and environmental factors, most of their answers referred to the potential of a wide diffusion for the improvement of farm efficiency and efficacy. However, two interviewees stated that they do not feel competent in understanding these interrelations due to their complexity. Working in a more technical discipline seems to reduce the capacity of researchers to understand how technologies and social factors interrelate.
In addition, researchers participating in the study stated that they are able to assess the degree to which their research activity on precision agriculture (e.g., development of tools that farmers and practitioners can use in their everyday practice, development of algorithms that will allow growers to gain more yield by using precision agriculture techniques) affects the future of farming, farmers’ wellbeing, and agrifood systems’ resilience. Nevertheless, the data revealed a potential positivity bias since researchers tend to view the impact of their work as mainly positive. An exemplary comment was the following: “I am highly confident that my research activities have a positive impact on all three of the aforementioned parameters.” However, three participants acknowledged the existence of open gaps that need to be filled. One of them commented: “There is always space for improvement”.
When researchers were asked to evaluate their training needs on a one-to-ten scale, understanding the interplay between technologies and socio-economic and environmental factors received the highest mean score. As Figure 2 highlights, the differences between means were small, since mean scores ranged from 7.2 to 8. However, how can researchers build skills in these areas? Most participants consider research and study of the relevant literature as the main ways to improve knowledge and gain skills related to precision agriculture. Interestingly, it seems that being active in the academic/research community for years is crucial for determining the “proper framework” within which knowledge and skills can emerge. Illustrative of this perception was the following comment: “I’m an academic, and academics believe they know everything through books. There must be another way to gain knowledge and build skills. Ask somebody else”.

4. Discussion

Our study aimed to identify the gaps in farmers’ precision agriculture-related skills. We also sought to provide some first insights into the competencies of researchers working in relevant fields of study. The approach followed led us to discover that farmers have moderate to high technology-related competencies, with lower scores on items concerning problems associated with integrating precision agriculture technologies into farms and their bundling with older technological tools, as well as their technology selection competencies. However, our analysis also uncovered a limited technology exploitation capacity on the part of farmers. Ingram and Maye’s [63] recent study arrived at a similar conclusion, attributing high priority to questions related to actors’ capacity to transform digital technologies into productive resources. As Vecchio et al. [60] note, a shift from the emphasis on “how technology works” to the real potential of technologies is essential to achieve such a purpose.
Furthermore, our results indicated that the farmers lack transition-related competencies, especially in the category of human-technology interaction. That is not surprising since, at the early stages of technology adoption, users may interact “their way” with artifacts, attributing and assigning different meanings to technological tools [64]. A finding deserving attention is that farmers’ competency in facilitating farm workers’ engagement with precision agriculture technologies is relatively low. Since farm workers might not possess the skills needed to utilize these technologies [65], farm owners should often act as intermediaries, supplying them with technical skills.
On the other hand, the analysis revealed that farmers have a high capacity to learn through their interaction with technology. Such “integrative learning” competency [66] can help them gradually sharpen technology exploitation skills and farm management competencies. Moreover, we found that farmers in our sample have high emotion-handling skills, supporting the relationship between self-control and technology adoption [67].
Our content analysis uncovered that the lack of agricultural education and training programs, along with their overemphasis on theory over practice, and the questionable competencies of trainers do not allow the upskilling of farmers who adopted precision agriculture technologies. The absence of other opportunities to enhance precision agriculture-related competencies (digital platforms, web-based material) is crucial for helping producers increase their overall level of competencies.
Concerning researchers, the findings suggest that they have considerable expertise in precision agriculture. However, their technical background and the lack of field expertise condition their competency to fully understand the impacts of precision agriculture beyond the farm level. As conceptual [41,68,69,70] and empirical contributions [45,71,72] from the field of digital agricultural technology implementation suggest, the interrelations between technologies and socio-environmental factors are not easily identifiable and require systemic thinking to be uncovered. The positivity bias in favor of precision agriculture technologies that we noticed can be explained in light of the research specializations represented in our sample. All the researchers participating in our study have a technical background, given that they are working on developing technologies. The lack of a social science lens probably reduces their ability to see the impacts of their work and the technologies they create through a holistic perspective [73].

5. Conclusions

To close, both surveyed groups have adequate technical competencies, but farmers have limited capacity to exploit and extract value from precision agriculture technologies. The results also stress the need to enhance farmers’ skills in anticipating and designing the future of their enterprises. On the other hand, researchers present shortcomings in competencies related to predicting how their research activity shapes the future of farming and impacts farmers’ well-being. Moreover, their understanding of how precision agriculture technologies interrelate with the social fabric of farming systems and what ecological and economic conditions determine their potential in promoting a paradigmatic shift of agriculture needs to be enhanced.
Although we acknowledge that, due to small sample sizes, these conclusions are not generalizable to the populations of Greek farmers and researchers—let alone to other countries—they offer an introductory view on the levels of the gaps in precision agriculture-related competencies that need to be covered through upskilling processes. As we state in the title of the present article, here we aimed to provide a first view of the competencies possessed by farmers and researchers in the field, knowing that as the digitalization of agriculture continues, new competency needs may emerge while, as a function of experience, some skills will improve. Future researchers can use larger samples, which can be subjected to inferential analyses to uncover potential differences among the examined competencies. Moreover, comparing competency needs between farmers applying different production systems (e.g., conventional versus organic) and owning small-, medium-, or large-scale farms or between technology-oriented researchers and social scientists researching precision agriculture was beyond the scope of the present study, but represents a promising future research path.

Author Contributions

Conceptualization, A.M., C.C. and E.D.L.; methodology, A.M., C.C. and E.D.L.; validation, T.B. and E.L.; formal analysis, A.M., C.C. and E.D.L.; investigation, T.B., E.L., A.P. and D.L.; writing—original draft preparation, A.M., C.C. and E.D.L.; writing—review and editing, A.M., C.C., T.B., E.L., A.P., D.L. and E.D.L.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the European Union, grant number 101056291.

Institutional Review Board Statement

Ethical review and approval were waived for this study since the study was conducted in accordance with the Declaration of Helsinki and the EU General Data Protection Regulation.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be available upon request by the first author after the completion of the BOOST project.

Acknowledgments

The study is part of an ongoing project titled “BOOSTing agribusiness acceleration and digital hub networking by an advanced training program on sustainable Precision Agriculture”. The research project is co-funded by the European Union. Project number: 101056291.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Robert, P. Characterization of soil conditions at the field level for soil specific management. Geoderma 1993, 60, 57–72. [Google Scholar] [CrossRef]
  2. Wallace, A. High-precision agriculture is an excellent tool for conservation of natural resources. Commun. Soil Sci. Plant Anal. 1994, 25, 45–49. [Google Scholar] [CrossRef]
  3. Karunathilake, E.M.B.M.; Le, A.T.; Heo, S.; Chung, Y.S.; Mansoor, S. The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture 2023, 13, 1593. [Google Scholar] [CrossRef]
  4. Tan, X.J.; Cheor, W.L.; Yeo, K.S.; Leow, W.Z. Expert systems in oil palm precision agriculture: A decade systematic review. J. King Saud Univ. Comput. Inf. Sci. 2022, 34, 1569–1594. [Google Scholar] [CrossRef]
  5. Duncan, E.; Glaros, A.; Ross, D.Z.; Nost, E. New but for whom? Discourses of innovation in precision agriculture. Agric. Hum. Values 2021, 38, 1181–1199. [Google Scholar] [CrossRef] [PubMed]
  6. Lowenberg-DeBoer, J.; Erickson, B. Setting the record straight on precision agriculture adoption. Agron. J. 2019, 111, 1552–1569. [Google Scholar] [CrossRef]
  7. Monzon, J.P.; Calviño, P.A.; Sadras, V.O.; Zubiaurre, J.B.; Andrade, F.H. Precision agriculture based on crop physiological principles improves whole-farm yield and profit: A case study. Eur. J. Agron. 2018, 99, 62–71. [Google Scholar] [CrossRef]
  8. Gebbers, R.; Adamchuk, V.I. Precision agriculture and food security. Science 2010, 327, 828–831. [Google Scholar] [CrossRef]
  9. McBratney, A.; Whelan, B.; Ancev, T.; Bouma, J. Future directions of precision agriculture. Precis. Agric. 2005, 6, 7–23. [Google Scholar] [CrossRef]
  10. Plant, R.; Pettygrove, G.; Reinert, W. Precision agriculture can increase profits and limit environmental impacts. Calif. Agric. 2000, 54, 66–71. [Google Scholar] [CrossRef]
  11. Yarashynskaya, A.; Prus, P. Precision agriculture implementation factors and adoption potential: The case study of Polish agriculture. Agronomy 2022, 12, 2226. [Google Scholar] [CrossRef]
  12. Song, C.; Zhou, Z.; Zang, Y.; Zhao, L.; Yang, W.; Luo, X.; Jiang, R.; Ming, R.; Zang, Y.; Zi, L.; et al. Variable-rate control system for UAV-based granular fertilizer spreader. Comput. Electron. Agric. 2021, 180, 105832. [Google Scholar] [CrossRef]
  13. Bhakta, I.; Phadikar, S.; Majumder, K. State-of-the-art technologies in precision agriculture: A systematic review. J. Sci. Food Agric. 2019, 99, 4878–4888. [Google Scholar] [CrossRef] [PubMed]
  14. Cisternas, I.; Velásquez, I.; Caro, A.; Rodríguez, A. Systematic literature review of implementations of precision agriculture. Comput. Electron. Agric. 2020, 176, 105626. [Google Scholar] [CrossRef]
  15. Groher, T.; Heitkämper, K.; Walter, A.; Liebisch, F.; Umstätter, C. Status quo of adoption of precision agriculture enabling technologies in Swiss plant production. Precis. Agric. 2020, 21, 1327–1350. [Google Scholar] [CrossRef]
  16. Torky, M.; Hassanein, A.E. Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Comput. Electron. Agric. 2020, 178, 105476. [Google Scholar] [CrossRef]
  17. Feng, X.; Yan, F.; Liu, X. Study of wireless communication technologies on Internet of Things for precision agriculture. Wirel. Pers. Commun. 2019, 108, 1785–1802. [Google Scholar] [CrossRef]
  18. McConnell, M.D. Bridging the gap between conservation delivery and economics with precision agriculture. Wild. Soc. Bull. 2019, 43, 391–397. [Google Scholar] [CrossRef]
  19. Thakur, D.; Kumar, Y.; Kumar, A.; Singh, P.K. Applicability of wireless sensor networks in precision agriculture: A review. Wirel. Pers. Commun. 2019, 107, 471–512. [Google Scholar] [CrossRef]
  20. Gargiulo, J.I.; Eastwood, C.R.; Garcia, S.C.; Lyons, N.A. Dairy farmers with larger herd sizes adopt more precision dairy technologies. J. Dairy Sci. 2018, 101, 5466–5473. [Google Scholar] [CrossRef]
  21. Silva, C.B.; do Vale, S.M.L.R.; Pinto, F.A.; Müller, C.A.; Moura, A.D. The economic feasibility of precision agriculture in Mato Grosso do Sul State, Brazil: A case study. Precis. Agric 2007, 8, 255–265. [Google Scholar] [CrossRef]
  22. Carrer, M.J.; de Souza Filho, H.M.; Vinholis, M.D.M.B.; Mozambani, C.I. Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil. Technol. Forecast. Soc. Chang. 2022, 177, 121510. [Google Scholar] [CrossRef]
  23. Zhang, Z.; Boubin, J.; Stewart, C.; Khanal, S. Whole-field reinforcement learning: A fully autonomous aerial scouting method for precision agriculture. Sensors 2020, 20, 6585. [Google Scholar] [CrossRef] [PubMed]
  24. Sgroi, F. Precision agriculture and competitive advantage: Economic efficiency of the mechanized harvesting of Chardonnay and Nero d’Avola grapes. J. Agric. Food Res. 2023, 14, 100774. [Google Scholar] [CrossRef]
  25. Lavorato, M.P.; Braga, M.J. Risk and return of soybeans precision production: A case study in Mato Grosso do Sul state, Brazil1. Ital. Rev. Agric. Econ. 2018, 73, 27–36. [Google Scholar] [CrossRef]
  26. Rakun, J.; Rihter, E.; Kelc, D.; Denis, S.; Vindiš, P.; Berk, P.; Polič, P.; Lakota, M. Possibilities and concerns of implementing precision agriculture technologies on small farms in Slovenia. Int. J. Agric. Biol. Eng. 2022, 15, 16–21. [Google Scholar] [CrossRef]
  27. Pierce, F.J.; Nowak, P. Aspects of precision agriculture. Adv. Agron. 1999, 67, 1–85. [Google Scholar] [CrossRef]
  28. Gumbi, N.; Gumbi, L.; Twinomurinzi, H. Towards sustainable digital agriculture for smallholder farmers: A systematic literature review. Sustainability 2023, 15, 12530. [Google Scholar] [CrossRef]
  29. Pathak, H.S.; Brown, P.; Best, T.A. A systematic literature review of the factors affecting the precision agriculture adoption process. Precis. Agric. 2019, 20, 1292–1316. [Google Scholar] [CrossRef]
  30. Wang, T.; Jin, H.; Sieverding, H.; Kumar, S.; Miao, Y.; Rao, X.; Obembe, O.; Mirzakhani Nafchi, A.; Redfearn, D.; Cheye, S. Understanding farmer views of precision agriculture profitability in the US Midwest. Ecol. Econ. 2023, 213, 107950. [Google Scholar] [CrossRef]
  31. Kolady, D.E.; Van der Sluis, E.; Uddin, M.M.; Deutz, A.P. Determinants of adoption and adoption intensity of precision agriculture technologies: Evidence from South Dakota. Prec. Agric. 2021, 22, 689–710. [Google Scholar] [CrossRef]
  32. Michels, M.; von Hobe, C.F.; Weller von Ahlefeld, P.J.; Musshoff, O. The adoption of drones in German agriculture: A structural equation model. Precis. Agric. 2021, 22, 1728–1748. [Google Scholar] [CrossRef]
  33. Miller, N.J.; Griffin, T.W.; Ciampitti, I.A.; Sharda, A. Farm adoption of embodied knowledge and information intensive precision agriculture technology bundles. Precis. Agric. 2019, 20, 348–361. [Google Scholar] [CrossRef]
  34. Higgins, V.; van der Velden, D.; Bechtet, N.; Bryant, M.; Battersby, J.; Belle, M.; Klerkx, L. Deliberative assembling: Tinkering and farmer agency in precision agriculture implementation. J. Rural Stud. 2023, 100, 103023. [Google Scholar] [CrossRef]
  35. da Silveira, F.; da Silva, S.L.C.; Machado, F.M.; Barbedo, J.G.A.; Amaral, F.G. Farmers’ perception of barriers that difficult the implementation of agriculture 4.0. Agric. Syst. 2023, 208, 103656. [Google Scholar] [CrossRef]
  36. Delavarpour, N.; Koparan, C.; Nowatzki, J.; Bajwa, S.; Sun, X. A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens. 2021, 13, 1204. [Google Scholar] [CrossRef]
  37. Schwering, D.S.; Bergmann, L.; Sonntag, W.I. How to encourage farmers to digitize? A study on user typologies and motivations of farm management information systems. Comput. Electron. Agric. 2022, 199, 107133. [Google Scholar] [CrossRef]
  38. Ciarli, T.; Kenney, M.; Massini, S.; Piscitello, L. Digital technologies, innovation, and skills: Emerging trajectories and challenges. Res. Policy 2021, 50, 104289. [Google Scholar] [CrossRef]
  39. Shepherd, M.; Turner, J.A.; Small, B.; Wheeler, D. Priorities for science to overcome hurdles thwarting the full promise of the ‘digital agriculture’revolution. J. Sci. Food Agric. 2020, 100, 5083–5092. [Google Scholar] [CrossRef]
  40. Liu, Y.; Ma, X.; Shu, L.; Hancke, G.P.; Abu-Mahfouz, A.M. From Industry 4.0 to Agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Trans. Ind. Inform. 2020, 17, 4322–4334. [Google Scholar] [CrossRef]
  41. McGrath, K.; Brown, C.; Regan, Á.; Russell, T. Investigating narratives and trends in digital agriculture: A scoping study of social and behavioural science studies. Agric. Syst. 2023, 207, 103616. [Google Scholar] [CrossRef]
  42. Gascuel-Odoux, C.; Lescourret, F.; Dedieu, B.; Detang-Dessendre, C.; Faverdin, P.; Hazard, L.; Litrico-Chiarelli, I.; Petit, S.; Roques, L.; Reboud, X.; et al. A research agenda for scaling up agroecology in European countries. Agron. Sustain. Dev. 2022, 42, 53. [Google Scholar] [CrossRef] [PubMed]
  43. Hackfort, S. Patterns of inequalities in digital agriculture: A systematic literature review. Sustainability 2021, 13, 12345. [Google Scholar] [CrossRef]
  44. Regan, Á. Exploring the readiness of publicly funded researchers to practice responsible research and innovation in digital agriculture. J. Responsible Innov. 2021, 8, 28–47. [Google Scholar] [CrossRef]
  45. Jakku, E.; Fleming, A.; Espig, M.; Fielke, S.; Finlay-Smits, S.C.; Turner, J.A. Disruption disrupted? Reflecting on the relationship between responsible innovation and digital agriculture research and development at multiple levels in Australia and Aotearoa New Zealand. Agric. Syst. 2023, 204, 103555. [Google Scholar] [CrossRef]
  46. Ingram, J.; Maye, D.; Bailye, C.; Barnes, A.; Bear, C.; Bell, M.; Cutress, D.; Davies, L.; de Boon, A.; Dinnie, L.; et al. What are the priority research questions for digital agriculture? Land Use Policy 2022, 114, 105962. [Google Scholar] [CrossRef]
  47. Lioutas, E.D.; Charatsari, C. Innovating digitally: The new texture of practices in agriculture 4.0. Sociol. Rural. 2022, 62, 250–278. [Google Scholar] [CrossRef]
  48. Leech, N.L.; Onwuegbuzie, A.J. A typology of mixed methods research designs. Qual. Quant. 2009, 43, 265–275. [Google Scholar] [CrossRef]
  49. Johnson, R.B.; Onwuegbuzie, A.J.; Turner, L.A. Toward a definition of mixed methods research. J. Mix. Methods Res. 2007, 1, 112–133. [Google Scholar] [CrossRef]
  50. Johnson, R.B.; Onwuegbuzie, A.J. Mixed methods research: A research paradigm whose time has come. Educ. Res. 2004, 33, 14–26. [Google Scholar] [CrossRef]
  51. Bolfe, É.L.; Jorge, L.A.D.C.; Sanches, I.D.A.; Luchiari Júnior, A.; da Costa, C.C.; Victoria, D.D.C.; Inamasu, R.Y.; Grego, C.R.; Ferreira, V.R.; Ramirez, A.R. Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture 2020, 10, 653. [Google Scholar] [CrossRef]
  52. Charatsari, C.; Lioutas, E.D. Is current agronomy ready to promote sustainable agriculture? Identifying key skills and competencies needed. Int. J. Sustain. Dev. World Ecol. 2019, 26, 232–241. [Google Scholar] [CrossRef]
  53. Thomas, K.V.; Murali, S. Validation and testing of a measurement model for the assessment of agripreneurial competencies. J. Agribusiness Dev. Emerg. Econ. 2023, in press. [Google Scholar] [CrossRef]
  54. Kwaghtyo, D.K.; Eke, C.I. Smart farming prediction models for precision agriculture: A comprehensive survey. Artif. Intell. Rev. 2023, 56, 5729–5772. [Google Scholar] [CrossRef]
  55. Prutzer, E.; Gardezi, M.; Rizzo, D.M.; Emery, M.; Merrill, S.; Ryan, B.E.; Oikonomou, P.D.; Alvez, J.P.; Adereti, D.T.; Anjum, R.; et al. Rethinking ‘responsibility’ in precision agriculture innovation: Lessons from an interdisciplinary research team. J. Responsible Innov. 2023, 10, 2202093. [Google Scholar] [CrossRef]
  56. Charatsari, C.; Lioutas, E.D.; De Rosa, M.; Papadaki-Klavdianou, A. Extension and advisory organizations on the road to the digitalization of animal farming: An organizational learning perspective. Animals 2020, 10, 2056. [Google Scholar] [CrossRef]
  57. Charatsari, C.; Lioutas, E.D.; Papadaki-Klavdianou, A.; Michailidis, A.; Partalidou, M. Farm advisors amid the transition to Agriculture 4.0: Professional identity, conceptions of the future and future-specific competencies. Sociol. Rural. 2022, 62, 335–362. [Google Scholar] [CrossRef]
  58. Fielke, S.; Bronson, K.; Carolan, M.; Eastwood, C.; Higgins, V.; Jakku, E.; Klerkx, L.; Nettle, R.; Regan, Á.; Rose, D.C.; et al. A call to expand disciplinary boundaries so that social scientific imagination and practice are central to quests for ‘responsible’ digital agri-food innovation. Sociol. Rural. 2022, 62, 151–161. [Google Scholar] [CrossRef]
  59. Lioutas, E.D.; Charatsari, C. Smart farming and short food supply chains: Are they compatible? Land Use Policy 2020, 94, 104541. [Google Scholar] [CrossRef]
  60. Vecchio, Y.; Di Pasquale, J.; Del Giudice, T.; Pauselli, G.; Masi, M.; Adinolfi, F. Precision farming: What do Italian farmers really think? An application of the Q methodology. Agric. Syst. 2022, 201, 103466. [Google Scholar] [CrossRef]
  61. Lajoie-O’Malley, A.; Bronson, K.; van der Burg, S.; Klerkx, L. The future(s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosyst. Serv. 2020, 45, 101183. [Google Scholar] [CrossRef]
  62. Hsieh, H.F.; Shannon, S.E. Three approaches to qualitative content analysis. Qual. Health Res. 2005, 15, 1277–1288. [Google Scholar] [CrossRef] [PubMed]
  63. Ingram, J.; Maye, D. “How can we?” the need to direct research in digital agriculture towards capacities. J. Rural Stud. 2023, 100, 103003. [Google Scholar] [CrossRef]
  64. Kant, V. Cyber-physical systems as sociotechnical systems: A view towards human–technology interaction. Cyber-Phys. Syst. 2016, 2, 75–109. [Google Scholar] [CrossRef]
  65. Ammann, J.; Umstätter, C.; El Benni, N. The adoption of precision agriculture enabling technologies in Swiss outdoor vegetable production: A Delphi study. Precis. Agric. 2022, 23, 1354–1374. [Google Scholar] [CrossRef] [PubMed]
  66. Mulder, M. A five-component future competence (5CFC) model. J. Agric. Educ. Ext. 2017, 23, 99–102. [Google Scholar] [CrossRef]
  67. Bukchin, S.; Kerret, D. The role of self-control, hope and information in technology adoption by smallholder farmers–A moderation model. J. Rural. Stud. 2020, 74, 160–168. [Google Scholar] [CrossRef]
  68. Charatsari, C.; Lioutas, E.D.; De Rosa, M.; Vecchio, Y. Technological innovation and agrifood systems resilience: The potential and perils of three different strategies. Front. Sustain. Food Syst. 2022, 6, 872706. [Google Scholar] [CrossRef]
  69. Bahn, R.A.; Yehya, A.A.K.; Zurayk, R. Digitalization for sustainable agri-food systems: Potential, status, and risks for the MENA region. Sustainability 2021, 13, 3223. [Google Scholar] [CrossRef]
  70. Bacco, M.; Barsocchi, P.; Ferro, E.; Gotta, A.; Ruggeri, M. The digitisation of agriculture: A survey of research activities on smart farming. Array 2019, 3, 100009. [Google Scholar] [CrossRef]
  71. Bustamante, M.J. Digital platforms as common goods or economic goods? Constructing the worth of a nascent agricultural data platform. Technol. Forecast. Soc. Chang. 2023, 192, 122549. [Google Scholar] [CrossRef]
  72. Zscheischler, J.; Brunsch, R.; Rogga, S.; Scholz, R.W. Perceived risks and vulnerabilities of employing digitalization and digital data in agriculture–Socially robust orientations from a transdisciplinary process. J. Clean. Prod. 2022, 358, 132034. [Google Scholar] [CrossRef]
  73. Lioutas, E.D.; Charatsari, C. Big data in agriculture: Does the new oil lead to sustainability? Geoforum 2020, 109, 1–3. [Google Scholar] [CrossRef]
Agriculture 14 00099 g001
Figure 1. A summary of the code maps used in the content analysis.
Figure 1. A summary of the code maps used in the content analysis.
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Figure 2. Mean scores of researchers’ training needs.
Figure 2. Mean scores of researchers’ training needs.
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Table 1. Mean scores and standard deviations of the items referring to farmers’ technology-related competencies.
Table 1. Mean scores and standard deviations of the items referring to farmers’ technology-related competencies.
ItemMean ScoreS.D.
Choosing appropriate technologies for my farm3.631.06
Estimating the costs and benefits of new technologies4.250.89
Introducing new technologies to my farm4.250.89
Properly using technologies 4.380.92
Reorganizing work after technology adoption4.630.74
Solving problems associated with newly introduced technologies3.630.92
Connecting precision agriculture technologies with traditionally used technologies3.631.30
Creating value from technologies4.250.71
Transforming technologies into productive resources4.250.89
Table 2. Mean scores and standard deviations of the items referring to farmers’ transition-related competencies.
Table 2. Mean scores and standard deviations of the items referring to farmers’ transition-related competencies.
ItemMean ScoreS.D.
Using technologies in a way that maximizes the benefits for my farm4.001.07
Using technologies in a way that minimizes the production cost4.130.99
Exploiting the full range of opportunities offered by technologies3.881.55
Making technology a part of my farm enterprise3.751.03
Planning how to effectively exploit the opportunities that technologies offer3.751.03
Taking well-calculated risks concerning future investments in relevant technologies4.001.41
Being agile in accommodating relevant complementary innovations in my farm 3.880.99
Handling my emotions when things go wrong4.250.71
Being capable of integrating these technologies into the way of doing business3.880.99
Being able to make changes when technologies don’t fit the purposes of my farm enterprise3.631.06
Helping farm workers exploit technologies for the benefit of my farm3.431.13
Understanding how farm workers feel about technologies and resolving potential conflicts3.431.27
Facilitate the collaboration of human actors and technologies in my farm enterprise3.880.99
Being able to learn while integrating technologies into my farm enterprise4.380.74
Learning how to learn from technologies4.380.74
Forecasting future scenarios for my farm4.000.76
Orienting myself and my enterprise to the future4.130.99
Anticipating the potential futures that technologies create3.751.03
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Michailidis, A.; Charatsari, C.; Bournaris, T.; Loizou, E.; Paltaki, A.; Lazaridou, D.; Lioutas, E.D. A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies. Agriculture 2024, 14, 99. https://doi.org/10.3390/agriculture14010099

AMA Style

Michailidis A, Charatsari C, Bournaris T, Loizou E, Paltaki A, Lazaridou D, Lioutas ED. A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies. Agriculture. 2024; 14(1):99. https://doi.org/10.3390/agriculture14010099

Chicago/Turabian Style

Michailidis, Anastasios, Chrysanthi Charatsari, Thomas Bournaris, Efstratios Loizou, Aikaterini Paltaki, Dimitra Lazaridou, and Evagelos D. Lioutas. 2024. "A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies" Agriculture 14, no. 1: 99. https://doi.org/10.3390/agriculture14010099

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