Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Yari Vecchio
 -- 1489 2023-05-26 22:51:26 |
2 format correction
Dean Liu
 Meta information modification 1489 2023-05-30 02:24:28 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Masi, M.; Di Pasquale, J.; Vecchio, Y.; Capitanio, F. Precision Farming. Encyclopedia. Available online: https://encyclopedia.pub/entry/44904 (accessed on 18 May 2024).
Masi M, Di Pasquale J, Vecchio Y, Capitanio F. Precision Farming. Encyclopedia. Available at: https://encyclopedia.pub/entry/44904. Accessed May 18, 2024.
Masi, Margherita, Jorgelina Di Pasquale, Yari Vecchio, Fabian Capitanio. "Precision Farming" Encyclopedia, https://encyclopedia.pub/entry/44904 (accessed May 18, 2024).
Masi, M., Di Pasquale, J., Vecchio, Y., & Capitanio, F. (2023, May 26). Precision Farming. In Encyclopedia. https://encyclopedia.pub/entry/44904
Masi, Margherita, et al. "Precision Farming." Encyclopedia. Web. 26 May, 2023.
Precision Farming
Edit
This entry is adapted from the peer-reviewed paper 10.3390/land12051084

Among the innovations in agriculture, precision farming (PF) certainly plays an important role. PF originated in the US in the late 1980s and 1990s, then it spread globally. Conceptualized as “Precision Agriculture” or “Site Specific Management”, it is also described as “Smart Farming” and “Digital Farming” as it is based on the use of smart technologies in agriculture and digital data management.

variable rate technology adoption barriers precision farming

1. Introduction

The introduction of innovative technologies in agriculture has been studied for decades. Formerly, several studies have delved into the topic trying to understand which are the barriers or drivers to technology diffusion [1]; latterly, others have tried to model the adoption and implementation processes themselves [2]. The main purpose of these studies was not only to encourage their diffusion, but also to foster a change in business visions and in the way production activities are organized. This effort can also be seen in the institutional support, which over time has characterized itself as a promoter of technology diffusion and facilitator to close the gap between provider supply and user demand [3]. A paradigmatic consequence of this is the funding policies implemented by global agricultural policies, such as the Farm Bill in the United States (US) and the Common Agricultural Policy (CAP) in the European Union (EU). It is precisely the latter, in its latest programming, 2023–2027, that has paid special attention to the role of innovations as a fundamental tool to achieve the ambitious goal of “producing more, polluting less”. In addition, a major chapter has been dedicated to the development of knowledge systems, or better known as Agricultural Knowledge Innovation Systems (AKIS), precisely to foster the diffusion of innovations, linked to economic support measures in rural development plans with the aim of simplifying access to technologies [4].
Among the innovations in agriculture, precision farming (PF) certainly plays an important role. PF originated in the US in the late 1980s and 1990s, then it spread globally. Conceptualized as “Precision Agriculture” or “Site Specific Management”, it is also described as “Smart Farming” and “Digital Farming” as it is based on the use of smart technologies in agriculture and digital data management [5]. PF applies principles, technologies, and strategies for differentiated management of internal plot variability, studied considering the interaction between the spatio-temporal component, the type of cultivation, and farm-specific agronomic management [6]. Farming can be compared to a dynamic system, whose qualitative-quantitative production depends on the use of techniques that allow a variable application of inputs according to the actual needs of the crops and the chemical-physical properties of the soil. Underlying the application of PF tools is a preliminary study of both spatial and temporal variability, with the aim of identifying and quantifying the intensity of one or more parameters. In this way, “homogeneous zones” are identified, thanks to which differentiated management is implemented within the plot [7]. The resulting advantages are related to a better optimization of outputs, input rationalization, cost reduction, and environmental benefits [8].
Precision farming encompasses the use of numerous technologies that have spread to different parts of the world with varying degrees of use. Studies that have attempted to measure the take-up rate of different technologies in different countries tend to show that the adoption is higher for guidance (such as Global Navigation Satellite systems) or recording technologies (such as soil and yield mapping), rather than reacting ones (such as variable rate nutrients, seeding, and pesticides) [9][10]. Furthermore, many studies [10][11] confirmed that, on average, North American farms are more likely to use VRT than European farms.

2. Theoretical Background

Variable rate application is a technology that finds application in agricultural operations, from tillage to harvesting. VRT “allows precise seeding, optimization on planting density and improved application rate efficiency of herbicides, pesticides and nutrients, resulting in cost reduction and reducing environmental impact” [12] (p. 13). Other applications are recorded in the field of weed control treatments, gypsum/lime application, and irrigation [13][14][15]. VRT represents the idea of precision agriculture well, by managing primary production based on the needs of the soil, the land, and the crops that are grown. VRT can be map- or sensor-based [15]. VRT based on the use of prescription maps (produced before the operations) varies the amount of product to be distributed according to the information in the prescription maps, which are the result of data from different acquisition systems (e.g., yield map, agronomic indices, satellite images, meteorological data, soil sampling, etc.). The other VRT methodology uses “on-the-go” sensors, which detect in real time during the operation the chemical characteristics of the soil and the phenological state of the crop. These data are sent to the reprocessing unit from which feedback is given to the actuator on the amount of input to be spread. Distribution therefore takes place by homogeneous zones, each of which corresponds to a precise dose: the on-board computer controls the actuator (hydraulic or electric), which will modulate the opening damper or the volumetric regulation system based on the different management zone [16].
Even though there are no global assessments of VRT use rates in the literature, Finger et al. [10] and Maloku [11] confirmed that, on average, North American farms are more likely to use VRT than European farms. In North America, on average the rate of use is 17% greater than in Europe, according to Nowak [17]. In the European market, Germany, Denmark, and the Netherlands were first countries interested to use this technology [18], while the Mediterranean countries have only recently begun to see the introduction of these instruments. Delving into the literature, most studies have been conducted in America. In the United States [19][20], VRT utilization rates hardly exceed 40%. Other studies, although residual, have been conducted in Florida, Alabama [21], and Kansas [22], where higher utilization rates are also recorded. Some studies have been carried out in the UK with utilization rates from 8% [23] to 16% [24], even based on the different variable rate application (fertilization, seeding, etc.) for different production orientations [25]. Other studies have been conducted in Australia, with variable rate technology utilization rates averaging 20% [15] or higher [26]. In Europe, researchers find studies conducted in Germany, Sweden, France, the Netherlands, Belgium, and Denmark [11]. Reichardt and Jürgens [27] reported during 2001–2006 that approximately one out of five PF adopters used VRT in Germany. In Denmark, the rate of VRT use across studies ranges from 7% to 37% [26][27][28].
According to Nowak [17], the variable rate application has grown at a slower rate compared to other PF technologies. Sunding and Zilberman [29] state that there is a significant latency between the introduction of an innovative technology into the market and its widespread use by farmers, so its adoption is not immediate. Several types of barriers to the adoption and diffusion of technological innovations have been cited [30], some of which focused on VRT [13][14][15].
The first barrier is economic and can be attributed to the high initial costs and subsequent training and tool implementation costs that end-users should bear [31][32]. Indeed, studies identified larger operators as more willing to adopt VRT given their capacity to absorb costs [33][34]. However, researchers [1][26][35] also showed a positive association between farm profits and VRT adoption, also underlined possibilities to reduce costs (i.e., when adopting VRT together with soil mapping).
In addition to economic barriers, there remain socio-economic, organizational, institutional, behavioral barriers [36][37]. Innovation can be influenced by socio-economic factors such as the user’s age, education level, gender, and degree of information [38][39]. Younger, better-educated, more knowledgeable about the costs and benefits of PF, and more optimistic farmers were more likely to utilize VRT [16].
Furthermore, the business organization and work intensity could not be compatible with the application of new technologies [40]. Limits to VRTs adoption have been technical issues related to equipment and software, access to services, and lack of compatibility of equipment with existing farming operations [15][31].
The institutional context itself can influence these choices. Literature describes physical barriers related to the agroecological context in which the new technology might operate. In fact, different VRT adoption rates can be identified based on the different agro-meteorological characteristics of an area [15][41], as well as on the basis of the type of cultivation [33]. Cultural barriers (i.e., habits, consumer choices, market uncertainty) [42][43], as well as limited institutional support [44] have been identified. Subsidies, as well as more indirect interventions such as information support can lead to increased adoption of VRT [1][34]. For example, Evans et al. [14] confirmed the importance of economic incentives to motivate growers to move to higher levels of variable rate irrigation adoption.
Vecchio et al. [37][39] also describe barriers related to the cognitive sphere, emphasizing the importance of the farmer’s perception, which is now no longer linked only to risk appetite or the expected benefits of technologies. Indeed, it is with the term “perceived complexity” that these authors encapsulate the most influential barriers in the adoption process. Other authors recall how the farmer’s perception in fact produces a positive or negative attitude towards adoption [45], which is often shaped by the socio-economic characteristics of the individual [46] and the social systems in which the technology operates. In this sense, more research to understand group behavior and collective action is required [44][47].

References

  1. Barnes, A.P.; Soto, I.; Eory, V.; Beck, B.; Balafoutis, A.; Sánchez, B.; Vangeyte, J.; Fountas, S.; van der Wal, T.; Gómez-Barbero, M. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy 2019, 80, 163–174.
  2. Vecchio, Y.; Masi, M.; Adinolfi, F. From the AKAP to AKAIE model to assess the uptake of technological innovations in the aquaculture sector. Rev. Aquac. 2023, 15, 772–784.
  3. Long, T.B.; Blok, V.; Coninx, I. Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: Evidence from the Netherlands, France, Switzerland and Italy. J. Clean. Prod. 2016, 112, 9–21.
  4. SCAR. Resilience and Transformation. Report of the 5th SCAR Foresight Exercise Expert Group: Natural Resources and Food Systems: Transitions towards a ‘Safe and Just’ Operating Space; European Commission—Directorate-General for Research and Innovation 2020 Healthy Planet: Brussels, Belgium, 2020.
  5. Saiz-Rubio, V.; Rovira-Más, F. From smart farming towards agriculture 5.0: A review on crop data management. Agronomy 2020, 10, 207.
  6. Pierce, F.J.; Nowak, P. Aspects of precision agriculture. Adv. Agron. 1999, 67, 1–85.
  7. Godwin, R.J.; Miller, P.C.H. A review of the technologies for mapping within-field variability. Biosyst. Eng. 2003, 84, 393–407.
  8. Shi, Y.; Chen, M.; Wang, X.; Yang, H.; Yu, H.; Hao, X. Efficiency analysis and evaluation of centrifugal variable-rate fertilizer spreading based on real-time spectral information on rice. Comput. Electron. Agric. 2023, 204, 107505.
  9. Lowenberg-DeBoer, J.; Erickson, B. Setting the record straight on precision agriculture adoption. Agron. J. 2019, 111, 1552–1569.
  10. Finger, R.; Swinton, S.M.; El Benni, N.; Walter, A. Precision farming at the nexus of agricultural production and the environment. Annu. Rev. Resour. Econ. 2019, 11, 313–335.
  11. Maloku, D. Adoption of precision farming technologies: USA and EU situation. SEA–Pract. Appl. Sci. 2020, 8, 7–14.
  12. European Parliament. Precision Agriculture: An Opportunity for EU Farmers—Potential Support with the CAP 2014–2020. Directorate—General for Internal Policies—Policy Department B Structural and Cohesion Policies. 2014. Available online: https://www.europarl.europa.eu/RegData/etudes/note/join/2014/529049/IPOL-AGRI_NT%282014%29529049_EN.pdf (accessed on 1 February 2023).
  13. Torbett, J.C.; Roberts, R.K.; Larson, J.A.; English, B.C. Perceived importance of precision farming technologies in improving phosphorus and potassium efficiency in cotton production. Precis. Agric. 2007, 8, 127–137.
  14. Evans, R.G.; LaRue, J.; Stone, K.C.; King, B.A. Adoption of site-specific variable rate sprinkler irrigation systems. Irrig. Sci. 2013, 31, 871–887.
  15. Robertson, M.J.; Llewellyn, R.S.; Mandel, R.; Lawes, R.; Bramley, R.G.V.; Swift, L.; Metz, N.; O’callaghan, C. Adoption of variable rate fertiliser application in the Australian grains industry: Status, issues and prospects. Precis. Agric. 2012, 13, 181–199.
  16. Larson, J.A.; Roberts, R.K.; English, B.C.; Larkin, S.L.; Marra, M.C.; Martin, S.W.; Paxton, K.W.; Reeves, J.M. Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production. Precis. Agric. 2008, 9, 195–208.
  17. Nowak, B. Precision agriculture: Where do we stand? A review of the adoption of precision agriculture technologies on field crops farms in developed countries. Agric. Res. 2021, 10, 515–522.
  18. Gumpertsberger, E.; Jürgens, C. Acceptance of Precision agriculture in Germany—Results of a Survey in 2001. In Proceedings of the 4th European Conference on Precision Agriculture, Berlin, Germany, 15 June 2003; pp. 259–264.
  19. Daberkow, S.G.; McBride, W.D. Adoption of precision agriculture technologies by US farmers. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MI, USA, 16–19 July 2000; American Society of Agronomy: Maddison, WI, USA; pp. 1–12.
  20. Kotsiri, S.; Rejesus, R.M.; Marra, M.C.; Velandia, M.M. Farmers’ perceptions about spatial yield variability and precision farming technology adoption: An empirical study of cotton production in 12 Southeastern states (No. 1371–2016–108966). In Proceedings of the 2011 Annual Meeting, Corpus Christi, TX, USA, 5–8 February 2011.
  21. Winstead, A.T.; Norwood, S.H.; Griffin, T.W.; Runge, M.; Adrian, A.M.; Fulton, J.; Kelton, J. Adoption and use of precision agriculture technologies by practitioners. In Proceedings of the 10th International Conference on Precision Agriculture, Denver, CO, USA, 18–21 July 2010; pp. 18–21.
  22. Griffin, T.W.; Yeager, E.A. How quickly do farmers adopt technology? A duration analysis. In Precision Agriculture’19; Wageningen Academic Publishers: Wageningen, The Netherlands, 2019; pp. 97–115.
  23. Soto, I.; Barnes, A.; Balafoutis, A.; Beck, B.; Sanchez, B.; Vangeyte, J.; Fountas, S.; Van der Wal, T.; Eory, V.; Gómez-Barbero, M. The Contribution of Precision Agriculture Technologies to Farm Productivity and the Mitigation of Greenhouse Gas Emissions in the EU; Publications Office of the European Union: Luxembourg, 2019; ISBN 978-92-79-92834-5.
  24. POST. Precision Farming. 2015. Available online: https://researchbriefings.parliament.uk/ResearchBriefing/Summary/POST-PN-0505 (accessed on 1 February 2022).
  25. DEFRA. Farm Practices Survey October 2012—Current Farming Issues. 2013. Available online: https://www.gov.uk/government/statistics/farm-practices-survey-october-2012-current-farming-issues (accessed on 1 February 2022).
  26. Schimmelpfennig, D. Farm Profits and Adoption of Precision Agriculture; (No. 1477–2016–121190); USDA: Washington, DC, USA, 2016.
  27. Reichardt, M.; Jürgens, C. Adoption and future perspective of precision farming in Germany: Results of several surveys among different agricultural target groups. Precis. Agric. 2009, 10, 73–94.
  28. Fountas, S.; Blackmore, S.; Ess, D.; Hawkins, S.; Blumhoff, G.; Lowenberg-Deboer, J.; Sorensen, C.G. Farmer experience with precision agriculture in Denmark and the US Eastern Corn Belt. Precis. Agric. 2005, 6, 121–141.
  29. Sunding, D.; Zilberman, D. The agricultural innovation process: Research and technology adoption in a changing agricultural sector. Handb. Agric. Econ. 2001, 1, 207–261.
  30. Antolini, L.S.; Scare, R.F.; Dias, A. Adoption of precision agriculture technologies by farmers: A systematic literature review and proposition of an integrated conceptual framework. In Proceedings of the IFAMA World Conference, St. Paul, MI, USA, 14–17 June 2015; pp. 14–17.
  31. Pedersen, S.M.; Fountas, S.; Blackmore, B.S.; Gylling, M.; Pedersen, J.L. Adoption and perspectives of precision farming in Denmark. Acta Agric. Scand. Sect. B Soil Plant Sci. 2004, 54, 2–8.
  32. del Río González, P. Analysing the factors influencing clean technology adoption: A study of the Spanish pulp and paper industry. Bus. Strategy Environ. 2005, 14, 20–37.
  33. Hanson, E.D.; Cossette, M.K.; Roberts, D.C. The adoption and usage of precision agriculture technologies in North Dakota. Technol. Soc. 2022, 71, 102087.
  34. Townsend, L.C.; Noble, C. Variable rate precision farming and advisory services in Scotland: Supporting responsible digital innovation? Sociol. Rural. 2022, 62, 212–230.
  35. Schimmelpfennig, D.; Ebel, R. Sequential adoption and cost savings from precision agriculture. J. Agric. Resour. Econ. 2016, 41, 97–115.
  36. Hoffman, A.J.; Henn, R. Overcoming the social and psychological barriers to green building. Organ. Environ. 2008, 21, 390–419.
  37. Vecchio, Y.; De Rosa, M.; Adinolfi, F.; Bartoli, L.; Masi, M. Adoption of precision farming tools: A context-related analysis. Land Use Policy 2020, 94, 104481.
  38. Capitanio, F.; Coppola, A.; Pascucci, S. Indications for drivers of innovation in the food sector. Br. Food J. 2009, 111, 820–838.
  39. Vecchio, Y.; De Rosa, M.; Pauselli, G.; Masi, M.; Adinolfi, F. The leading role of perception: The FACOPA model to comprehend innovation adoption. Agric. Econ. 2022, 10, 5.
  40. Aubert, B.A.; Schroeder, A.; Grimaudo, J. IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decis. Support Syst. 2012, 54, 510–520.
  41. Zhou, X.; English, B.C.; Larson, J.A.; Lambert, D.M.; Roberts, R.K.; Boyer, C.N.; Velandia, M.; Falconer, L.L.; Martin, S.W. Precision farming adoption trends in the southern US. J. Cotton Sci. 2017, 21, 143–155.
  42. Ceschin, F. Critical factors for implementing and diffusing sustainable product-Service systems: Insights from innovation studies and companies’ experiences. J. Clean. Prod. 2013, 45, 74–88.
  43. Costa-Campi, M.T.; Duch-Brown, N.; Garcia-Quevedo, J. R&D drivers and obstacles to innovation in the energy industry. Energy Econ. 2014, 46, 20–30.
  44. Thompson, B.; Morrison, R.; Stephen, K.; Eory, V.; Ferreira, J.; Vigors, B.; Degiovanni, H.B.; Barnes, A.; Toma, L. Behaviour Change and Attitudes in the Scottish Agricultural Sector—A Rapid Evidence Assessment; SRUC: Aberdeen, UK, 2021.
  45. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340.
  46. Miyamoto, Y.; Yoo, J.; Levine, C.S.; Park, J.; Boylan, J.M.; Sims, T.; Markus, H.R.; Kitayama, S.; Kawakami, N.; Coe, C.L.; et al. Culture and social hierarchy: Self-and other-oriented correlates of socioeconomic status across cultures. J. Pers. Soc. Psychol. 2018, 115, 427.
  47. Joffre, O.M.; De Vries, J.R.; Klerkx, L.; Poortvliet, P.M. Why are cluster farmers adopting more aquaculture technologies and practices? The role of trust and interaction within shrimp farmers’ networks in the Mekong Delta, Vietnam. Aquaculture 2020, 523, 735181.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Agricultural Economics & Policy
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Margherita Masi
,
Jorgelina Di Pasquale
,
Yari Vecchio
,
Fabian Capitanio
View Times: 321
Revisions: 2 times (View History)
Update Date: 30 May 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback