Willingness to pay for smartphone apps facilitating sustainable crop protection

  • Vanessa BonkeEmail author
  • Wilm Fecke
  • Marius Michels
  • Oliver Musshoff
Research Article


By providing additional information and simulating results, decision support tools are one of the methods to enhance a farmer’s decision-making process in order to achieve more sustainable practices. With the latest developments in smartphone technology, new possibilities to integrate decision support tools into the daily work process have been emerging and smartphone apps related to crop protection have been developed. However, little is known about the utilization of smartphones by farmers in general, and specifically with regard to crop protection. In order to gather first insights into the factors that could affect the decision of farmers to integrate smartphones and crop protection-related apps in particular, into their work process, we conducted an online survey with 174 technologically experienced German farmers in 2017. We gained insights about the current use of smartphones from the surveyed German farmers, explored which topics farmers perceive as useful in the form of an app for crop protection, and which factors influence the willingness to pay for these apps. Our results show that 93% of the respondents use smartphones for agricultural purposes. Weather forecasts, tools to identify pests, diseases and weeds, as well as related forecasts are perceived as useful by the majority of respondents. Eighty-two percent of the respondents are generally willing to pay for crop protection apps. Using a probit model, we found that the farmer’s age, farm size, knowledge about specific crop protection apps, potential for cost reduction, and potential to reduce negative environmental effects have an influence on the general willingness to pay. Overall, this is the first study to explore factors influencing the willingness to pay for crop protection apps and assess which types of apps are perceived as useful by technologically experienced German farmers.


Smartphone Crop protection apps Willingness to pay Perceived usefulness Probit model German farmers 



The authors would like to thank two anonymous referees and the editors for helpful comments and suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Batte MT (2005) Changing computer use in agriculture: evidence from Ohio. Comput Electron Agric 47:1–13. CrossRefGoogle Scholar
  2. Briggeman BC, Whitacre BE (2010) Farming and the internet: reasons for non-use. J Agric Resour Econ 39(3):571–584. CrossRefGoogle Scholar
  3. CEJA (2017) European Young Farmers survey - Building a sustainable sector.
  4. Curto JD, Pinto JC (2011) The corrected VIF (CVIF). J Appl Stat 38(7):1499–1507. CrossRefGoogle Scholar
  5. Damos P (2015) Modular structure of web-based decision support systems for integrated pest management. A review. Agron Sustain Dev 35(4):1345–1372. CrossRefGoogle Scholar
  6. DBV (2016) (German Farmers Federation) Situationsbericht 2016/17–03 Agrarstruktur. Berlin.
  7. Dehnen-Schmutz K, Foster GL, Owen L, Persello S (2016) Exploring the role of smartphone technology for citizen science in agriculture. Agron Sustain Dev 36:25. CrossRefGoogle Scholar
  8. Dentzmann K (2018) “I would say that might be all it is, is hope”: the framing of herbicide resistence and how farmers explain their faith in herbicides. J Rural Stud 57:118–127. CrossRefGoogle Scholar
  9. EU (2009) Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for Community action to achieve sustainable use of pesticides.
  10. EU SCAR (2012) Agricultural knowledge and innovation systems in transition – a reflection paper. Brussels.
  11. European Commission (2013) Structure and dynamics of EU farms: changes, trends and policy relevance. EU Agricultural Economic Briefs No 9.
  12. Eurostat (2016) Agriculture, forestry and fishery statistics – 2016 edition.
  13. Evans KJ, Terhorst A, Hang BH (2017) From data to decisions: helping crop producers build their actionable knowledge. Crit Rev Plant Sci 36(2):71–88. CrossRefGoogle Scholar
  14. Gent DH, De Wolf E, Pethybridge SJ (2011) Perceptions of risk, risk aversion, and barriers to adoption of decision support systems and integrated pest management: an introduction. Phytopathology 101(6):640–643. CrossRefPubMedGoogle Scholar
  15. Greene WH (2007) Econometric analysis, 6th edn. Pearson Education, Upper Saddle RiverGoogle Scholar
  16. Hoffmann C, Grethler D, Doluschitz R (2013) Mobile business: good preconditions on farms. Landtechnik 68(1):18–21Google Scholar
  17. Janssen SJC, Porter CH, Moore AD, Athanasiadis IN, Foster I, Jones JW, Antle JM (2017) Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agric Syst 155:200–212. CrossRefPubMedPubMedCentralGoogle Scholar
  18. Lefebvre M, Langrell SRH, Gomez-y-Paloma S (2015) Incentives and policies for integrated pest management in Europe: a review. Agron Sustain Dev 35:27–45. CrossRefGoogle Scholar
  19. Lindblom J, Lundström C, Ljung M, Jonsson A (2017) Promoting sustainable intensifications in precision agriculture: review of decision support systems development and strategies. Precis Agric 18:309–331. CrossRefGoogle Scholar
  20. Matthews KB, Schwarz G, Buchan K, Rivington M, Millder D (2008) Wither agricultural DSS? Comput Electron Agric 61:149–159. CrossRefGoogle Scholar
  21. Mishra AK, Park T (2005) An empirical analysis of internet use by U.S. farmers. J Agric Resour Econ 34(2):253–264. CrossRefGoogle Scholar
  22. 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. Precis Agric 10:525–545. CrossRefGoogle Scholar
  23. Rose DC, Sutherland WJ, Parker C, Lobley M, Winter M, Morris C, Twining S, Floulkes C, Amano T, Dicks LV (2016) Decision support tools for agriculture: towards effective design and delivery. Agric Syst 149:165–174. CrossRefGoogle Scholar
  24. Rose DC, Parker C, Fodey J, Park C, Sutherland WJ, Dicks LV (2018) Involving stakeholders in agricultural decision support systems: improving user-centered design. Int J Agric Manag 6(3–4):80–89. CrossRefGoogle Scholar
  25. Shtienberg D (2013) Will decision-support systems be widely used for the management of plant diseases? Annu Rev Phytopathol 51:1–16. CrossRefPubMedGoogle Scholar
  26. Spash CL, Urama K, Burton R, Kenyon W, Shannon P, Hill G (2009) Motives behind willingness to pay for improving biodiversity in a water ecosystem: economics, ethics and social psychology. Ecol Econ 68(4):955–964. CrossRefGoogle Scholar
  27. Spaulding AD, Tudor KW, Mahatanankoon P (2015) The effects of outcome expectations on individual’s anxiety and continued usage of mobile devices: a post-adoption study. Int Food Agribus Manag Rev 18(4):173–188Google Scholar
  28. Struik PC, Kuyper TW (2017) Sustainable intensification in agriculture: the richer shade of green. A review. Agron Sustain Dev 37:39. CrossRefGoogle Scholar
  29. Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36(1):157–178CrossRefGoogle Scholar
  30. Xin J, Zazueta FS, Vergott P III, Mao X, Kooram N, Yang Y (2015) Delivering knowledge and solutions at your fingertips: strategy for mobile app development in agriculture. Agric Eng Int CIGR J Spec Issue 2015:317–325Google Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department for Agricultural Economics and Rural DevelopmentGeorg-August-Universität GöttingenGöttingenGermany

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