SKILLS DEVELOPMENT IN PRECISION AGRICULTURE, CHALLENGES AND OPPORTUNITIES

Introduction

Abstract

Precision Agriculture (PA) represents an approach to farm management that uses information technology and a wide array of items like GPS guidance, control systems, sensors, robotics, drones, autonomous vehicles, variable rate technology, GPS-based soil sampling, and automated hardware to optimize field-level management regarding crop farming. This method aims to ensure profitability, sustainability, and land resource protection. Precision agriculture is characterized by its ability to provide real-time data that assists farmers in making informed decisions regarding the planting, cultivating, and harvesting crops.

Key elements include soil variability, weather conditions, crop yield data, and pest activities, which are vital for making precise farming decisions. By leveraging these elements, farmers can significantly reduce the number of chemical inputs like pesticides and fertilizers, thus lowering production costs and environmental impact. Despite its numerous benefits, the adoption rate of precision agriculture technologies is affected by various barriers, including high initial costs, lack of technical expertise, and limited access to reliable data and digital literacy.

This paper tries to answer the following questions:

·????? What are the most common definitions of precision agriculture? What does it involve? What is the connection between Industrial Revelation 4.0 and digital agriculture?

·????? What are the main challenges and barriers preventing the adoption of precision agriculture in skills development and education? What approach should be used to identify the required skills for this technology? What improvement measures should be taken?

Furthermore, the paper discusses the importance of integrating educational programs and policy support to promote the adoption of precision agriculture technologies. Educational initiatives to increase farmers’ digital literacy can help them understand and utilize precision agriculture technologies more effectively.

Overall, Precision Agriculture offers a promising future for the agricultural sector by enhancing efficiency and sustainability. However, its success depends on overcoming existing barriers and promoting widespread adoption through education and policy support.

Main Findings

As Revelation 4.0 is the industry’s future, precision agriculture is the future of the agriculture sector today and in the future, according to the advantages of precision agriculture, such as improved efficiency and sustainability in farming practices. Nevertheless, it points out significant obstacles like high costs, lack of technical know-how, and insufficient infrastructure.

Also, the paper provides a more profound examination, identifying a broader array of influencing factors on precision agriculture adoption. They discuss the importance of understanding farmers’ motivations and perceptions, shaped by a complex mix of socioeconomic conditions (like financial capacity and the economic impacts of adopting new technologies), agroecological factors (such as soil quality), and institutional support. Many studies stress the critical roles of access to knowledge, educational resources, and farmers’ attitudes toward technology.

The analysis extends to the educational requirements for fostering a skilled workforce in precision agriculture. Moreover, the study highlights the need for educational programs covering agronomic skills and including technological and entrepreneurial training. It is argued that such a multidisciplinary approach is essential for successfully implementing precision agriculture systems. The paper also notes the slow and long-term nature of precision agriculture technology adoption among farmers.

The criticism of existing studies points to a need for comprehensive skill set identification necessary for precision agriculture application. It argues that beyond IT skills and public awareness, there needs to be a more straightforward definition of the enabling environment for precision agriculture and exploration of free and accessible resources that could assist farmers in addressing issues on their farms.

Furthermore, this paper underscores the necessity of addressing the technological gap in agricultural education and enhancing the training in precision agriculture to include not just traditional farming skills but also data management and analysis, which are pivotal for maximizing the benefits of precision agriculture technologies. Given the data-centric nature of precision agriculture, this approach aligns with the perspective that modern farmers should be viewed as data scientists.

In conclusion, the detailed analysis presents a scenario where the adoption of precision agriculture is hindered by a complex web of barriers, requiring a multifaceted approach to overcome. This approach includes enhancing the educational framework, developing a comprehensive skill set among farmers and agricultural professionals, and ensuring the socio-economic and technological environments are conducive to adopting precision agricultural practices.

Recommendation

In a world increasingly reliant on sustainable and efficient farming practices, highlighting the urgent need to transcend traditional barriers within the agricultural sector. This paper proposes a paradigm shift, envisioning an educational landscape that embraces and is tailored specifically to the multifaceted demands of contemporary agriculture. The crux of this transformative vision rests on implementing holistic and skill-intensive educational frameworks, moving beyond mere agrarian techniques to encompass a wide array of competencies critical for the modern farmer.

The advocacy for an innovative approach where interactive and remote learning modalities break down geographical and economic constraints, democratizing knowledge access. It highlights a future where the curriculum is not just about soil and seeds but extends to embrace data analysis, technological fluency, and entrepreneurial skills, recognizing these elements as indispensable in the era of precision agriculture.

This educational reform underscores the necessity for clarity in defining the specific skills required to harness the full potential of precision agriculture effectively. Also, the roadmap to transformative education in this domain should be comprehensive, incorporating insights from psychology, social sciences, and economics to enrich the learning experience and outcomes.

Moreover, the digitalization of education emerges as a powerful tool, offering a cost-effective means to disseminate knowledge and foster a culture of continuous learning. The paper suggests extending this vision by advocating for broader inclusion and targeting an audience for these programs, which should not be limited to traditional farming roles but expanded to include specialists in management, information technology, sustainability, and development sciences.

Investing in precision agriculture is depicted as a catalyst for innovation and entrepreneurship among the younger generations, thereby revitalizing the agricultural sector. The paper suggests further exploration of the symbiotic relationship between small and medium-sized enterprises (SMEs) specializing in technical services and consultancy for farmers, emphasizing their critical role in shaping a sustainable and prosperous agricultural future.

Finally, the human element is critical; thus, promoting the development of interpersonal skills is just as vital as technical proficiency for the success of any skills development initiative in precision agriculture. This educational transformation and sectoral innovation paint a hopeful picture of a future where agriculture feeds bodies and nurtures minds and communities, steering the sector towards a more sustainable, efficient, and inclusive horizon.

Definition of Precision Agriculture

The most common definition of Precision Agriculture (PA) is “a management strategy that gathers, processes and analyses temporal-spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” (Bournaris et al. 2022, 2). Also, precision agriculture defined as “a holistic, sustainable, innovative systems approach that assists farmers in production management.” (Lee, Strong, and Dooley 2021, 1). And, that system “involves crop management according to field variability and site-specific parameters” (Seelan et al. 2003, 157–169).

This system is established on a technological system that includes “new tools and sources of information provided by modern technologies. These include the global positioning system (GPS), geographic information systems (GIS), yield monitoring devices, soil, plant, and pest sensors, remote sensing, and variable-rate technologies for applicators of inputs (Seelan et al. 2003).

Moreover, some studies defined this system as “an information and technology-based farm management system to identify, analyze, and manage spatial and temporal variability within fields for optimum productivity and profitability, sustainability, and protection of the land resource by minimizing production costs.” (Demirba? 2018, 1–2).

Furthermore, the study showed the importance of this approach to achieve sustainability for the future of the food through “Efficient use of water, reduction of soil erosion and degradation to the minimum, minimization of energy input and maximization of yields under uncertain natural conditions are the goal.” (Godfray et al. 2010).

In the last decade, precision agriculture “has attracted more attention as a solution in food production to feed a growing population.” (Godfray et al. 2010, 812–18)

Furthermore, many researchers connected precision agriculture with Revelation 4.0. For example, the academic article (Zambon et al. 2019) described the relationship between Revolution 4.0 and precision agriculture. The authors defined the “Industry 4.0 approach permitted the creation of an environment in which all elements are continuously and effortlessly linked. All devices (e.g., CPS, cyber-physical systems) and functionalities are addressed as services, which constantly communicate with each other, thus achieving a high level of coordination” (Zambon et al. 2019, 1). The integration of precision agriculture with the Internet of farming gives rise to Agriculture 4.0, also known as digital agriculture, which links various technologies to enhance crop yields and sustainability, improve working conditions, and elevate the quality of production and processing (Zambon et al. 2019, 9)

As a result, all the provided definitions focus on the ultimate result or impact of using this system on responding to the emerging needs in the agriculture sector in terms of sustainable production. Also, the definition above shows that precision agriculture includes different fields of science, such as agriculture, management, information technology, sustainability, and others. At the same time, the complexity of this approach can bring many challenges in adopting this system in the agriculture field.

Skills Development in Precision Agriculture

The precision agriculture approach faces many barriers to adoption in the agriculture sector. This section focuses on the skills development system required for precision agriculture adoption and the gaps in this field. In addition to the required skill set, it identifies the educational system gaps preventing its application.

The Food and Agriculture Organization of the United Nations FAO published the book (Ceccarelli et al. 2022), which describes how digital and automation solutions for precision agriculture can improve efficiency, productivity, product quality, and sustainability. Nevertheless, barriers to adopting such solutions include cost, lack of knowledge and skills, an enabling environment, and infrastructure that can prevent producers from realizing these benefits.

Furthermore, in this study, the authors mentioned the required skills; they interviewed 13 farmers in different locations and regions throughout the study, and they identified the lack of digital literacy, awareness, and knowledge as the main barriers to precision agriculture adoption (Ceccarelli et al. 2022, 21).

This study failed to identify the skill sets required to apply precision agriculture. This approach needs more than IT skills and public awareness to be adapted. Furthermore, the study must define the environment that will enable this approach. Moreover, the analysis ignores the accessible and free open sources so that the farmer can quickly determine their issues regarding their farms. Precision agriculture is an agricultural system that uses technology as a tool for the benefit of agricultural production. Also, the sample size must be more significant to get accurate indicators.

The academic research (Bournaris et al. 2022, 2–3) mentioned that adopting Precision Agriculture technologies among farmers is a slow and long-term process. They said it is essential to understand the myriad factors that motivate farmers and shape their perceptions of precision agriculture’s benefits. These factors encompass a wide array of considerations, including socioeconomic conditions, which relate to the financial capacity and economic impact of adopting new technologies; agroecological concerns, which involve natural conditions like soil quality that may affect precision agriculture’s applicability; and institutional frameworks, reflecting the role of governmental and other bodies in supporting or hindering precision agriculture adoption.

Additionally, informational factors such as access to knowledge and educational resources about precision agriculture and farmers’ perceptions regarding the technology’s benefits and risks play a significant role. Behavioral aspects, including the attitudes and willingness of farmers to embrace new technologies, along with technological factors like the complexity and accessibility of precision agriculture systems, also critically influence adoption rates.

Furthermore, the article pointed out the high initial and maintenance costs associated with precision agriculture, the steep learning curve, the level of existing agricultural education, the complexity of the technology, the availability of consultancy and advisory services, uncertainties regarding outcomes, variables like the farmer’s age and farm size, household income, and specific agroecological parameters such as soil texture and quality. All these elements collectively impact farmers’ decision-making when considering the adoption of precision agriculture practices, highlighting the complex interplay of factors in agricultural innovation.

Also, the article (Bournaris et al. 2022, 3–4) mentioned that the successful implementation of Precision Agriculture faces several barriers, including a lack of knowledge and expertise, significant capital investments, data protection issues, and technology incompatibility. The rapid expansion of precision agriculture necessitates education for farmers and agribusiness professionals facilitated through educational programs and extension services. There is a critical need for the precision agriculture sector to develop a skilled workforce to keep pace with precision agriculture’s evolving landscape. High-quality educational materials are essential for equipping students with new skills and abilities in precision agriculture. The qualification level of the students involved significantly enhances the effectiveness of precision agriculture adoption. There is a shift towards more long-distance education programs in response to the changing educational landscape. Success in precision agriculture hinges on improved student education and accessible advisory services for farmers. Alongside enhancing training courses, assessing training needs is crucial. However, it is challenging to determine if agricultural university curricula are currently meeting the needs of the precision agriculture labor market.

The study shows that agricultural students in four countries in the Euro-Mediterranean region are interested in learning more about precision agriculture and enhancing their technical and personal skills. Moreover, the selected samples highlighted the importance of learning new skills rather than agronomic skills, such as technological and entrepreneurial skills, to adopt the precision agriculture best practices (Bournaris et al. 2022, 11). Also, the reference (Bournaris et al. 2022) discussed the required precision agriculture training for agriculture students in the Euro-Mediterranean region. This study showed the differences in the students’ precision agriculture training and pointed out the importance of setting the socioeconomic characteristics of the targeted students. “Results support the notion that the lack of “precision agriculture knowledge/interest” adds to the technological gap amongst university students, slow adoption of precision agriculture, and lower levels of overall rural economic development.” (Bournaris et al. 2022, 1).

In contrast to (Ceccarelli et al. 2022), the academic article (Bournaris et al. 2022) provided a comprehensive understanding of the current situation of the precision agriculture adoption challenges; also, it gave an accurate analytic analysis of the weak adoption process and it identified the main root factor by covering different dimensions such as the socioeconomic, ?financial capacity, economic impact, and agroecological concerns. That deep understanding of the problem’s roots will allow the researchers to define better how the future skills development program can be designed and what should be considered to improve the educational system.

Also, the case study mentioned the importance of promoting this technology, such as how it can create and deliver messages to the farmers. Although the case study mentioned the required skills and areas that should be approved in the educational system, such as agronomic, technological, and entrepreneurial skills, which are still insufficient for the adoption process, and it is critical for people working in the field to integrate social science for the success of the adoption process.??

Although the study highlighted the importance of expanding the range of end users to include other specializations, such as consultancy services, in addition to the agriculture student, precision agriculture should target information technology, management, development, and economy students, as this system includes many dimensions rather than the agronomic.

Other notes: the case study covered only specific countries in the Mediterranean region. This concludes limited to understanding some of the difficulties experienced by different countries from the same area, such as North Africa, or another region, such as the tropics. Also, the sample was from agriculture students, which could include students from other fields.

Another piece of information is that research conducted in the US shows that farmers who adopt precision agriculture are younger, more educated, full-time farmers and employ larger-scale farms (Demirba? 2018, 4). This is exciting information about the opportunities to integrate youth in this field. At the same time, adoption was only for large farms. This conclusion was noticed previously due to the lack of awareness about this technology and misleading messages regarding the cost. ???

According to the results of this study (Watcharaanantapong 2012) that analyzes the adaptation of precision agriculture of the farmer and farm characteristics conducted in the US, the level of adopting the three selected precision agriculture of younger farmers has higher yields in the production of cotton, have more taxable income and currently use computer or laptop for business management, were found to be higher than other farmers. Similar to the previous reference, this study shows young people’s potential participation in the future adoption process.

The reference (Demirba? 2018) states that the adoption of data-driven agriculture food management strategies for Canada is currently limited to innovative farmers or early adopters, and the bottlenecks that prevent more widespread acceptance are ease of use and integration difficulties.

Furthermore, a study from Australia revealed that 74% of farmers need help understanding the terms and conditions associated with their data contracts. Additionally, over 62% of these farmers lack confidence in how their service providers handle farm data (Demirba? 2018).

Moreover, the methods and data gathering necessary for Precision Agriculture Technologies, like variable ratio applications, demand particular expertise to maximize the use of their data. Access to individuals and teams proficient in data management systems and spatial data analysis is highly beneficial. However, numerous extension staff need more guidance on employing these technologies (Demirba? 2018).

The academic article (Duncan et al. 2021, 13) provides an essential note that digital devices, often hailed as transformative, have limited effectiveness without human intervention: even if a farm is equipped with the most advanced digital tools and is fully mechanized, the diligent and precise efforts of humans remain crucial for success. Smart farming cannot replace the essential role of farmers. Another important note: “We have to face the fact that most yield monitor data does not get used. It is too difficult, and farmers do not have the time or skills to do it” (Duncan et al. 2021, 13)

In precision agriculture, this approach is based on data-supporting decision-making. The decision support system needs unique skill sets that involve many scientific fields and personal competencies.

For example, the academic article (Lindblom et al. 2017) associates the low reception of the Decision Support System (DSS) among farmers with the need for more engagement between technology developers and farmers. Concerning the ‘‘problem of implementation’’, Several reasons have been proposed for the low adoption rate of Agriculture Decision Support Systems (AgriDSSs), including differing characteristics of individual farms, the age of the farmer, and the level of educational attainment (Lindblom et al. 2017, 5)

Furthermore, it has been suggested that the factors affecting the uptake of innovations are closely associated with work practices that extend beyond the simple frameworks of technology acceptance or the spread of innovations. The study defined the obstacles to adopting AgriDSS in precision agriculture as time demands, insufficient technical knowledge, and high costs. A significant issue is that researchers tend to concentrate on a particular area or issue. In contrast, farmers must adopt a comprehensive approach to crop production, addressing a broad spectrum of challenges (Lindblom et al. 2017, 5).

Another critical academic article by (Gardezi et al. 2022, 226) mentioned that the preparation of the workforce with the required skills to use the Decision Support System efficiently presents various challenges, including high costs for training and equipment, limited broadband internet access in rural regions, a deficiency in mathematical skills among undergraduate students, and issues of comfort and trust among farmers.

However, cultivating new abilities means doing something other than disregarding or deeming farmers’ implicit knowledge as outdated. Instead, it involves delicately handling the fusion of workers’ existing knowledge with the enhancements that DSSs can provide. (Gardezi et al. 2022, 238)

The authors conducted field research to validate their findings. In the focus group discussions (FGDs), participants evaluated the risks and advantages of precision agriculture instruments like DSSs, AI, big data, and machine learning algorithms for agricultural, livestock, and dairy decision-making. They also examined the impact of current precision agriculture educational programs and discussed how conventional and unconventional educational approaches could equip farmers and technical staff for roles in precision agriculture. Through these FGDs, participants expressed their views and shared their experiences regarding creating, applying, or regulating precision agriculture technologies (Gardezi et al. 2022, 232).

?The main findings of the mentioned fieldwork are that the transition from data to information and knowledge alters the skill set needed in agriculture. For instance, an Extension worker from a university in Vermont views a competent farmer as one who adopts data science practices: The most adept farmers constantly collect observational data. While they might not see themselves as data scientists, they act as collectors of information (Gardezi et al. 2022, 233).

Also, the provided references show the robust connection between precision agriculture and the Decision Support System. Based on the description above, the required skills for the adoption include a wide range of skill sets that should be integrated into different areas of science such as technology, agriculture, social and psychology, and other fields. Moreover, the researchers highlighted the importance of the farmers’ participation in this process as leading players, not only as beneficiaries of this system.

Conclusion

The detailed analysis presents a scenario where the adoption of precision agriculture is hindered by a complex web of barriers, requiring a multifaceted approach to overcome. This approach includes enhancing the educational framework, developing a comprehensive skill set among farmers and agricultural professionals, and ensuring the socio-economic and technological environments are conducive to adopting PA practices. The text advocates for an integrated educational strategy encompassing a broad spectrum of skills beyond traditional farming techniques, emphasizing the importance of data management, technological proficiency, and entrepreneurial acumen in the modern agricultural landscape.

References

Bournaris, Thomas, Manuela Correia, Alessandro Guadagni, Jeremy Karouta, Anne Krus, Stefania Lombardo, Dimitra Lazaridou, et al. 2022. “Current Skills of Students and Their Expected Future Training Needs on Precision Agriculture: Evidence from Euro-Mediterranean Higher Education Institutes.” Agronomy 12 (2): 269. https://doi.org/10.3390/agronomy12020269.

Ceccarelli, T, A Chauhan, G Rambaldi, I Kumar, C Cappello, C Janssen, and M McCampbell. 2022. Leveraging Automation and Digitalization for Precision Agriculture: Evidence from the Case Studies. FAO. https://doi.org/10.4060/cc2912en.

Demirba?, Nevin. 2018. Precision Agriculture in Terms of Food Security: Needs for The Future.

Duncan, Emily, Alesandros Glaros, Dennis Z. Ross, and Eric Nost. 2021. “New but for Whom? Discourses of Innovation in Precision Agriculture.” Agriculture and Human Values 38 (4): 1181–99. https://doi.org/10.1007/s10460-021-10244-8.

Gardezi, Maaz, Damilola Tobiloba Adereti, Ryan Stock, and Ayorinde Ogunyiola. 2022. “In Pursuit of Responsible Innovation for Precision Agriculture Technologies.” Journal of Responsible Innovation 9 (2): 224–47. https://doi.org/10.1080/23299460.2022.2071668.

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Lee, Chin-Ling, Robert Strong, and Kim E. Dooley. 2021. “Analyzing Precision Agriculture Adoption across the Globe: A Systematic Review of Scholarship from 1999–2020.” Sustainability 13 (18): 10295. https://doi.org/10.3390/su131810295.

Lindblom, Jessica, Christina Lundstr?m, Magnus Ljung, and Anders Jonsson. 2017. “Promoting Sustainable Intensification in Precision Agriculture: Review of Decision Support Systems Development and Strategies.” Precision Agriculture 18 (3): 309–31. https://doi.org/10.1007/s11119-016-9491-4.

Seelan, Santhosh K, Soizik Laguette, Grant M Casady, and George A Seielstad. 2003. “Remote Sensing Applications for Precision Agriculture: A Learning Community Approach.” Remote Sensing of Environment, IKONOS Fine Spatial Resolution Land Observation, 88 (1): 157–69. https://doi.org/10.1016/j.rse.2003.04.007.

Watcharaanantapong, Pattarawan. 2012. “FACTORS INFLUENCING PRECISION FARMING TECHNOLOGY ADOPTION OVER TIME IN SOUTHERN U.S. COTTON PRODUCTION.” University of Tennessee.

Zambon, Ilaria, Massimo Cecchini, Gianluca Egidi, Maria Grazia Saporito, and Andrea Colantoni. 2019. “Revolution 4.0: Industry vs. Agriculture in a Future Development for SMEs.” Processes 7 (1): 36. https://doi.org/10.3390/pr7010036.

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