Advertisement

Precision Agriculture

, Volume 18, Issue 5, pp 701–716 | Cite as

Adoption of precision agriculture technologies by German crop farmers

  • Margit Paustian
  • Ludwig Theuvsen
Article

Abstract

In recent years, precision farming has been receiving more attention from researchers. Precision farming, which provides a holistic system approach, helps farmers to manage the spatial and temporal crop and soil variability within a field in order to increase profitability, optimize yield and quality, and reduce costs. There has been considerable research in farmers’ adoption of precision agriculture technologies. However, most recent studies have considered only a few aspects, whereas in this study a wide range of farm characteristics and farmer demographics are tested to gain insight into the relevant aspects of adoption of precision farming in German crop farming. The results of a logistic regression analysis show that predictors with positive influence on the adoption of precision farming are agricultural contractor services such as an additional farming business, having under 5 years’ experience in crop farming, having between 16 and 20 years’ experience in crop farming, and having more than 500 ha of arable land. However, having a farm of less than 100 ha and producing barley are factors that exert a negative influence on the adoption of precision farming. The results of this study provide manifold starting points for the further proliferation of precision agriculture technologies and future research directions.

Keywords

Precision farming Technology adoption Binary logistic regression model Socio-demographic factors Farm characteristics 

References

  1. Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision Support Systems, 54, 510–520.CrossRefGoogle Scholar
  2. Auernhammer, H. (2001). Precision farming: The environmental challenge. Computers and Electronics in Agriculture, 30, 31–43.CrossRefGoogle Scholar
  3. Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2011). Multivariate analysemethoden: Eine anwendungsorientierte einführung [Multivariate analysis methods: A application-oriented introduction] (13th ed.). Heidelberg: Springer.Google Scholar
  4. Batte, M. T., & Arnholt, M. W. (2003). Precision farming adoption and use in Ohio: Case studies of six leading-edge adopters. Computers and Electronics in Agriculture, 38, 125–139.CrossRefGoogle Scholar
  5. Bramley, R. G. V. (2009). Lessons from nearly 20 years of precision agriculture research, development, and adoption as a guide to its appropriate application. Crop and Pasture Science, 60, 197–217.CrossRefGoogle Scholar
  6. Busse, M., Doernberg, A., Siebert, R., Kuntosch, A., Schwerdtner, W., König, B., et al. (2014). Innovation mechanisms in German precision farming. Precision Agriculture, 15, 403–426.CrossRefGoogle Scholar
  7. Clasen, M. (2016). Farming 4.0 und andere anwendungen des internet der dinge. In Ruckelshausen, A. et al. (Eds.), Proceedings of GIL annual meeting 2016. Informatik in der Land-, Forst- und Ernährungswirtschaft. Fokus: Intelligente Systeme—Stand der Technik und neue Möglichkei-ten (pp. 15–18). Bonn: Koellen.Google Scholar
  8. Cox, S. (2002). Information technology: The global key to precision agriculture and sustainability. Computers and Electronics in Agriculture, 36, 93–111.CrossRefGoogle Scholar
  9. Daberkow, S. G., & McBride, W. D. (2003). Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision Agriculture, 4, 163–177.CrossRefGoogle Scholar
  10. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London: Sage.Google Scholar
  11. Forschungsgruppe Wahlen. (2014). Internet-Strukturdaten IV. Quartal 2013. Retrieved November 30, 2015 from http://www.bvdw.org/medien/forschungsgruppe-wahlen-internet-strukturdaten-iv-quartal-2013?media=5465
  12. Fountas, S., Blackmore, S., Ess, D., Hawkins, S., Blumhoff, G., Lowenberg-De Boer, J., et al. (2005). Farmers experience with precision agriculture in Denmark and the US eastern corn belt. Precision Agriculture, 6, 121–141.CrossRefGoogle Scholar
  13. Jensen, H. G., Jacobsen, L.-B., Pedersen, S. M., & Tavella, E. (2012). Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Denmark. Precision Agriculture, 13, 661–677.CrossRefGoogle Scholar
  14. Khanna, M. (2001). Sequential adoption of site-specific technologies and its implications for nitrogen productivity: A double selectivity model. American Journal of Agricultural Economics, 83(1), 35–51.CrossRefGoogle Scholar
  15. Kitchen, N. R., Snyder, C. J., Franzen, D. W., & Wiebold, W. J. (2002). Educational needs of precision agriculture. Precision Agriculture, 3, 341–351.CrossRefGoogle Scholar
  16. König, B., Kuntosch, A., Bokelmann, W., Doernberg, A., Schwerdtner, W., Busse, M., et al. (2012). Nachhaltige innovationen in der landwirtschaft: Komplexe herausforderungen im innovationssystem [Sustainable innovation in agriculture: Complex challenges in the innovation system]. Vierteljahreshefte zur Wirtschaftsforschung, 81(4), 71–92. doi: 10.3790/vjh.81.4.71.CrossRefGoogle Scholar
  17. Kröger, R., Konerding, J. R., & Theuvsen, L. (2016). Identifikation von Einflussfaktoren auf die Nutzung von Güllefeststoffen als Gärsubstrat in Biogasanlagen [Identification of factors that influence the use of manure solids as a fermentation substrate in biogas plants]. German Journal of Agricultural Economics, 65, 112–131.Google Scholar
  18. Kutter, T., Tiemann, S., Siebert, R., & Fountas, S. (2011). The role of communication and co-operation in the adoption of precision farming. Precision Agriculture, 12, 2–17.CrossRefGoogle Scholar
  19. Lambert, D. M., English, B. C., Harper, D. C., Larkin, S. L., Larson, J. A., Mooney, D. F., et al. (2014). Adoption and frequency of precision soil testing in cotton production. Journal of Agricultural and Resource Economics, 39(1), 106–123.Google Scholar
  20. Larson, J. A., Roberts, R. K., English, B. C., Larkin, S. L., Marra, M. C., Martin, S. W., et al. (2008). Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production. Precision Agriculture, 9, 195–208.CrossRefGoogle Scholar
  21. Lencsés, E., Takács, I., & Takács-György, K. (2014). Farmers’ perception of precision farming technology among Hungarian farmers. Sustainability, 6, 8452–8465. doi: 10.3390/su6128452.CrossRefGoogle Scholar
  22. Mackrell, D., Kerr, L., & Von Hellens, A. (2009). A qualitative case study of the adoption and use of an agricultural decision support system in the Australian cotton industry: The socio-technical view. Decision Support Systems, 47, 143–153.CrossRefGoogle Scholar
  23. McBratney, A., Whelan, B., Ancev, T., & Bouma, J. (2005). Future directions of precision agriculture. Precision Agriculture, 6, 7–23.CrossRefGoogle Scholar
  24. McBride, W. D., & Daberkow, S. G. (2003). Information and the adoption of precision farming technologies. Journal of Agribusiness, 21(1), 21–38.Google Scholar
  25. Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013). Drivers of precision agriculture technologies adoption: A literature review. Procedia Technology, 8, 61–69.CrossRefGoogle Scholar
  26. Reichardt, M., & Jürgens, C. (2009). Adoption and future perspective of precision farming in Germany: Results of several surveys among different agricultural target groups. Precision Agriculture, 10, 73–94.CrossRefGoogle Scholar
  27. Reichardt, M., Jürgens, C., Klöble, U., Hüter, J., & Moser, K. (2009). Dissemination of precision farming in Germany: Acceptance, adoption, obstacles, knowledge transfer and training activities. Precision Agriculture, 10, 525–545.CrossRefGoogle Scholar
  28. Roberts, R. K., English, B. C., Larson, J. A., Cochran, W. R., Goodman, W. R., Larkin, S. L., et al. (2004). Adoption of site-specific information and variable-rate technologies in cotton precision farming. Journal of Agricultural and Applied Economics, 36(1), 143–158.CrossRefGoogle Scholar
  29. Robertson, M., Carberry, P., & Brennan, L. (2009). Economic benefits of variable rate technology: Case studies from Australian grain farms. Crop and Pasture Science, 60, 799–807.CrossRefGoogle Scholar
  30. Schoengold, K., & Sunding, D. L. (2014). The impact of water price uncertainty on the adoption of precision irrigation systems. Agricultural Economics, 45, 729–743.CrossRefGoogle Scholar
  31. Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76(3), 267–275.CrossRefGoogle Scholar
  32. Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture, 13, 713–730.CrossRefGoogle Scholar
  33. Tey, Y. S., Li, E., Bruwer, J., Abdullah, A. M., Brindal, M., Radam, A., et al. (2014). The relative importance of factors influencing the adoption of sustainable agricultural practices: A factor approach for Malaysian vegetable farmers. Sustainability Science, 9(1), 17–29.CrossRefGoogle Scholar
  34. Torbett, J. C., Roberts, R. K., Larson, J. A., & English, B. C. (2007). Perceived importance of precision farming technologies in improving phosphorus and potassium efficiency in cotton production. Precision Agriculture, 8, 127–137.CrossRefGoogle Scholar
  35. Walton, J. C., Lambert, D. M., Roberts, R. K., Larson, J. A., English, B. C., & Larkin, B. C. (2008). Adoption and abandonment of precision soil sampling in cotton production. Journal of Agricultural and Resource Economics, 33(3), 428–448.Google Scholar
  36. Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture: A worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Agricultural Economics and Rural DevelopmentGeorg-August-University GoettingenGöttingenGermany

Personalised recommendations