User-Technological Index of Precision Agriculture

DOI 10.7160/aol.2017.090106
No 1/2017, March
pp. 69-75

Jarolímek, J., Stočes, M., Masner, J., Vaněk, J., Šimek, P., Pavlík, J. and Rajtr, J. (2017) “User-Technological Index of Precision Agriculture", AGRIS on-line Papers in Economics and Informatics, Vol. 9, No. 1, pp. 69 - 75. ISSN 1804-1930. DOI 10.7160/aol.2017.090106.


User-Technological Index of Precision Agriculture (UTIPA) is a comprehensive system based on mutual sharing of opinions and experience within community of people related to precision agriculture - farmers, technology suppliers and researchers. The main benefit of UTIPA is the possibility to use the calculated index level for particular technology (method) for precision agriculture and compare it to other technology with regards to different users, crops, regions etc. It evaluates the principle of a technology but does not take into account concrete products, brands or manufacturers. The index has significance for the presentation of the potential of precision agriculture, development planning and especially for the connection between technological innovativeness and usefulness for practice.The entire solution includes the methodology for the collection, processing and presentation of data and software and is available via a Web interface for all common device platforms. Anyone who has interest in precision agriculture and contributes their knowledge can use the collected data.


Precision agriculture, technological sophistication, user accessibility, knowledge sharing.


  1. Akdemir, B., Ungor, M. G., Saglam, N., Aydogdu, B., Belliturk, K., Kesici, E. and Urusan, A. (2014) „Development of a precision farming system for Turkish farmers“, Acta Horticulturae, Vol. 1054, pp. 301-308. [Online]. Available: [Accessed: Jan 20, 2017]. ISSN 0567-7572. DOI 10.17660/ActaHortic.2014.1054.36.
  2. Cambouris, A. N., Zebarth, B. J., Ziadi, N. and Perron I. (2014) „Precision Agriculture in Potato Production“, Potato Research, Vol. 57, No. 3-4, pp. 249-262. [Online]. Available: [Accessed: Jan 15, 2017]. ISSN 0014-3065. DOI 10.1007/s11540-014-9266-0.
  3. Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A, Liakos, B., Canavari, M., Wiebensohn, J. and Tisserye, B. (2015) „Farm management information systems: Current situation and future perspectives“, Computers and Electronics in Agriculture, Vol. 115, pp. 40-50. [Online]. Available: [Accessed: Feb 5, 2017]. ISSN 01681699. DOI 10.1016/j.compag.2015.05.011.
  4. Holpp, M., Kroulík, M., Kvíz, Z., Anken, T., Sauter, M. and Hensel, O. (2013) „Large-scale field evaluation of driving performance and ergonomic effects of satellite-based guidance systems“, Biosystems engineering, Vol. 116, No. 2, pp. 190-197. ISSN 1537-5110. DOI 10.1016/j.biosystemseng.2013.07.018.
  5. Katalin, T. G., Rahoveanu, T., Magdalena, M. and István, T. (2014) „Sustainable New Agricultural Technology – Economic Aspects of Precision Crop Protection“, Procedia Economics and Finance, Vol. 8, pp. 729-736. [Online]. Available: http://linkinghub. [Accessed: Jan 15, 2017]. ISSN 22125671. DOI 10.1016/S2212-5671(14)00151-8.
  6. Kroulík, M., Kumhála, F., Hůla, J.and Honzík, I. (2009) „The evaluation of agricultural machines field trafficking intensity for different soil tillage technologies“, Soil & Tillage Research, Vol. 105, No. 1, pp. 171-175. ISSN 0167-1987. DOI 10.1016/j.still.2009.07.004.
  7. Kumhála, F., Kroulík, M., Mašek, J. and Prošek, V. (2003) „Development and testing of two methods for the measurement of the mowing machine feed rate“, Plant Soil Environ, Vol. 49, No. 11, pp. 519-524. ISSN 1214-1178. DOI 10.17221/4187-PSE.
  8. Lee, S., JO, J. and Kim, Y. (2014) “Performance testing of web-based data visualization“, In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1648-1653. ISBN 978-1-4799-3840-7. DOI 10.1109/SMC.2014.6974152.
  9. Lindblom, J., Lundstrőm, Ch., Ljung, M. and Jonsson, A. (2016) „Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies“, Precision Agriculture. [Online]. Available: s11119-016-9491-4 [Accessed: Jan 10, 2017]. ISSN 1385-2256. DOI 10.1007/s11119-016-9491-4.
  10. Nette Fundation. [Online]. Available: [Accessed 12 December 2016].
  11. Paustian, M. and Theuvsen, L. (2016) „Adoption of precision agriculture technologies by German crop farmers“, Precision Agriculture. [Online]. Available: http://link.springer. com/10.1007/s11119-016-9482-5 [Accessed: Dec 15, 2016]. ISSN 1385-2256. DOI 10.1007/s11119-016-9482-5.
  12. Rakestraw, R. (2016) „Precision Ag Innovation Hype Curve“. [Online]. Available: https://drive. [Accessed: Jan 9, 2017]
  13. Shen, S., Basist A.and Howard, A. (2010) „Structure of a digital agriculture system and agricultural risks due to climate changes“, Agriculture and Agricultural Science Procedia, Vol. 1, pp. 42-51. [Online]. Available: [Accessed: Jan 10, 2017]. ISSN 22107843. DOI 10.1016/j.aaspro.2010.09.006.
  14. Schimmelpfennig, D. and Ebel, R. (2016) „Sequential Adoption and Cost Savings from Precision Agriculture“, Journal of Agricultural and Resource Economics, Vol. 41, No. 1, pp. 97-115. ISSN 1068-5502.
  15. Šmejkalova, M. Benda, P. and Ulman, M. (2015) “Usability and Accessibility analysis of Czech agrarian portals“. In Agrarian Perspectives XXIV. – Global Agribusiness and Rural Economy, Prague, pp. 456-462. ISBN 978-80-213-2581-4.
  16. Tey, Y. S., and Brindal, M. (2012) „Factors influencing the adoption of precision agricultural technologies: a review for policy implications“, Precision Agriculture, Vol. 13, No. 6, pp. 713-730. [Online]. Available: [Accessed: Jan 8, 2017]. ISSN 1385-2256. DOI 10.1007/s11119-012-9273-6.
  17. Wang, J., Zhang, M., Yang, X., Long, K and Xu, J. (2015) „HTTP-sCAN: Detecting HTTP- flooding attack by modeling multi-features of web browsing behavior from noisy web-logs“, China Communications, Vol. 12, No. 2, pp. 118-128. ISSN 1673-5447. DOI 10.1109/CC.2015.7084407.

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