Skip to main content
SpringerLink
Log in

Understanding the adoption of smartphone apps in crop protection

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

There is a steady increase in smartphone apps available to improve farmers’ decision making with respect to crop protection. While current studies have focused on smartphone adoption in general and farmers’ general willingness to pay for crop protection smartphone apps in particular, none have focused on the initial adoption decision. Furthermore, it has not been studied yet which app functions are perceived as useful and which are actually used by farmers. Based on an online survey conducted in 2019 with 207 German farmers, this study investigated latent factors affecting farmers’ adoption decision for crop protection smartphone apps based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework applying partial least squares equation modelling and a binary logit model. Descriptive results show that 95% of the surveyed farmers use a smartphone, but only 71% use a crop protection smartphone app. Apps providing information about weather, pest scouting and infestations forecasts are perceived as most useful by the majority of farmers. However, reported use fell short of reported usefulness. With respect to the model for the UTAUT, 73% of the variation in the behavioral intention to use a crop protection smartphone app is explained by the model. The results are of interest for policy makers in the field of digitization in agriculture as well as providers and developers of crop protection smartphone apps since the results could be used for further development of apps and policies regarding digitization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Ambrosius, F. H. W., Hofstede, J. G., Bock, B. B., Bokkers, E. A. M., & Beulens, A. J. M. (2015). Modelling farmer decision-making: The case of the Dutch pork sector. British Food Journal, 117(10), 2582–2597.

    Google Scholar 

  • Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., et al. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163–174.

    Google Scholar 

  • Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human-Computer Interaction, 34, 882–893.

    Google Scholar 

  • Baumüller, H. (2017). Towards smart farming? Mobile technology trends and their potential for developing country agriculture. In K. E. Skouby, I. Williams, & A. Gyamfi (Eds.), Handbook for ICT in developing countries: 5G perspectives (pp. 191–201). Gistrup, Denmark: River Publishers.

    Google Scholar 

  • Bonke, V., Fecke, W., Michels, M., & Musshoff, O. (2018). Willingness to pay for smartphone apps facilitating sustainable crop protection. Agronomy for Sustainable Development. https://doi.org/10.1007/s13593-018-0532-4.

    Article  Google Scholar 

  • Brudermann, T., Reinsberger, K., Orthofer, A., Kislinger, M., & Posch, A. (2013). Photovoltaics in agriculture: A case study on decision making of farmers. Energy Policy, 61, 96–103.

    Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–358). Mahwah, NJ, USA: Lawrence Erlbaum Associates.

    Google Scholar 

  • Damos, P. (2015). Modular structure of web-based decision support systems for integrated pest management: A review. Agronomy for Sustainable Development, 35(4), 1347–1372.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Google Scholar 

  • Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.

    CAS  Google Scholar 

  • Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.

    Google Scholar 

  • Evans, K. J., Terhorst, A., & Kang, B. H. (2017). From data to decisions: Helping crop producers build their actionable knowledge. Critical Reviews in Plant Sciences, 36(2), 71–88.

    Google Scholar 

  • Fielding, K. S., Terry, D. J., Masser, B. M., & Hogg, M. A. (2008). Integrating social identity theory and the theory of planned behaviour to explain decisions to engage in sustainable agricultural practices. British Journal of Social Psychology, 47(1), 23–48.

    Google Scholar 

  • Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., et al. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40–50.

    Google Scholar 

  • Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107.

    Google Scholar 

  • German Farmers‘ Federation (2019a). Drei von vier Landwirten wünschen sich schnellere Internet-Anbindung (Three out of four farmers want faster Internet connection). Retrieved April 2, 2020 from https://www.bauernverband.de/presse-medien/pressemitteilungen/pressemitteilung/dbv-drei-von-vier-landwirten-wuenschen-sich-schnellere-internet-anbindung.

  • German Farmers' Federation (2019b). Situationsbericht 2018/2019 (Situation report 2018/2019). Retrieved June 26, 2019, from https://www.bauernverband.de/situationsbericht-2018/19.

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, California, USA: Sage Publications.

    Google Scholar 

  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.

    Google Scholar 

  • Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.

    Google Scholar 

  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.

    Google Scholar 

  • Hallau, L., Neumann, M., Klatt, B., Kleinhenz, B., Klein, T., Kuhn, C., et al. (2018). Automated identification of sugar beet diseases using smartphones. Plant Pathology, 67(2), 399–410.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

    Google Scholar 

  • Hoffmann, C., Al Askari, A., Hoang, K., & Doluschitz, R. (2014). Development trends in agricultural apps—an interim review. LANDTECHNIK: Agricultural Engineering, 69(5), 250–255.

    Google Scholar 

  • Hoffmann, C., Grethler, D., & Doluschitz, R. (2013). Mobile business: Good preconditions on farms. LANDTECHNIK: Agricultural Engineering, 68(1), 18–21.

    Google Scholar 

  • Inwood, S. E. E., & Dale, V. H. (2019). State of apps targeting management for sustainability of agricultural landscapes: A review. Agronomy for Sustainable Development. https://doi.org/10.1007/s13593-018-0549-8.

    Article  Google Scholar 

  • Kabbiri, R., Dora, M., Kumar, V., Elepu, G., & Gellynck, X. (2018). Mobile phone adoption in agri-food sector: Are farmers in Sub-Saharan Africa connected? Technological Forecasting and Social Change, 131, 253–261.

    Google Scholar 

  • Khanna, A., & Kaur, S. (2019). Evolution of internet of things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218–231.

    Google Scholar 

  • Lindblom, J., Lundström, C., Ljung, M., & Jonsson, A. (2017). Promoting sustainable intensification in precision agriculture: Review of decision support systems development and strategies. Precision Agriculture, 18(3), 309–331.

    Google Scholar 

  • Marra, M. C., Rejesus, R. M., Roberts, R. K., English, B. C., Larson, J. A., Larkin, S. L., et al. (2010). Estimating the demand and willingness-to-pay for cotton yield monitors. Precision Agriculture, 11(3), 215–238.

    Google Scholar 

  • Michels, M., Bonke, V., & Musshoff, O. (2019). Understanding the adoption of herd management smartphone apps. Journal of Dairy Science, 102(10), 9422–9434.

    CAS  Google Scholar 

  • Michels, M., Fecke, W., Feil, J.-H., Musshoff, O., Pigisch, J., & Krone, S. (2020). Smartphone adoption and use in agriculture: Empirical evidence from Germany. Precision Agriculture, 21(2), 403–442.

    Google Scholar 

  • Nagelkerke, N. J. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692.

    Google Scholar 

  • Nansen, C., Ferguson, J. C., Moore, J., Groves, L., Emery, R., Garel, N., et al. (2015). Optimizing pesticide spray coverage using a novel web and smartphone tool, SnapCard. Agronomy for Sustainable Development, 35(3), 1075–1085.

    Google Scholar 

  • Nejadrezaei, N., Allahyari, M. S., Sadeghzadeh, M., Michailidis, A., & El Bilali, H. (2018). Factors affecting adoption of pressurized irrigation technology among olive farmers in Northern Iran. Applied Water Science, 8(190), 1–9.

    CAS  Google Scholar 

  • Paustian, M., & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18(5), 701–716.

    Google Scholar 

  • Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013). Drivers of precision agriculture technologies adoption: A literature review. Procedia Technology, 8, 61–69.

    Google Scholar 

  • Pignatti, E., Carli, G., & Canavari, M. (2015). What really matters? A qualitative analysis on the adoption of innovations in agriculture. Agrárinformatika/Journal of Agricultural Informatics, 6(4), 73–84.

    Google Scholar 

  • Pongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: A systematic review of research. Journal of Sensors. https://doi.org/10.1155/2015/195308.

    Article  Google Scholar 

  • 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(1), 73–94.

    Google Scholar 

  • Ringle, C.M., Wende, S., & Becker, J.-M. (2015). SmartPLS3. Boenningsted, Germany: SmartPLS GmbH. Retrieved April 26, 2019, from https://www.smartpls.com/.

  • Rose, D. C., Parker, C., Fodey, J. O., Park, C., Sutherland, W. J., & Dicks, L. V. (2018). Involving stakeholders in agricultural decision support systems: Improving user-centred design. International Journal of Agricultural Management, 6(3–4), 80–89.

    Google Scholar 

  • Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., et al. (2016). Decision support tools for agriculture: Towards effective design and delivery. Agricultural Systems, 149, 165–174.

    Google Scholar 

  • Schaak, H., & Musshoff, O. (2018). Understanding the adoption of grazing practices in German dairy farming. Agricultural Systems, 165, 230–239.

    Google Scholar 

  • Sønderskov, M., Fritzsche, R., de Mol, F., Gerowitt, B., Goltermann, S., Kierzek, R., et al. (2015). DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions–Field experimental results. Crop Protection, 76, 15–23.

    Google Scholar 

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133.

    Google Scholar 

  • Struik, P. C., & Kuyper, T. W. (2017). Sustainable intensification in agriculture: The richer shade of green: A review. Agronomy for Sustainable Development. https://doi.org/10.1007/s13593-017-0445-7.

    Article  Google Scholar 

  • Tamirat, T. W., Pedersen, S. M., & Lind, K. M. (2018). Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany. Acta Agriculturae Scandinavica, Section B—Soil Plant Science, 68(4), 349–357.

    Google Scholar 

  • Tey, Y. S., & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture, 13(6), 713–730.

    Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186–204.

    Google Scholar 

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  • Verbeek, M. (2008). A Guide to Modern Econometrics. Chichester, West Sussex, UK: Wiley.

    Google Scholar 

  • Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126, 207–216.

    Google Scholar 

  • Walker, D. A., & Smith, T. J. (2016). Nine pseudo R2 indices for binary logistic regression models. Journal of Modern Applied Statistical Methods, 15(1), 848–854.

    Google Scholar 

  • Wang, B. R., Park, J.-Y., Chung, K., & Choi, I. Y. (2014). Influential factors of smart health users according to usage experience and intention to use. Wireless Personal Communications, 79(4), 2671–2683.

    Google Scholar 

  • Wasan, P. G., & Jain, N. (2017). Customizing content for rural mobile phones: A study to understand the user needs of rural India. Social Network Analysis and Mining, 7(12), 1–13.

    Google Scholar 

  • Wright, D., Hammond, N., Thomas, G., MacLeod, B., & Abbott, L. K. (2018). The provision of pest and disease information using Information Communication Tools (ICT); an Australian example. Crop Protection, 103, 20–29.

    Google Scholar 

  • Yuan, S., Ma, W., Kanthawala, S., & Peng, W. (2015). Keep using my health apps: Discover users' perception of health and fitness apps with the UTAUT2 model. Telemedicine and e-Health, 21(9), 735–741.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agricultural Economics and Rural Development, Georg-August-University Göttingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany

    Marius Michels, Vanessa Bonke & Oliver Musshoff

Authors
  1. Marius Michels
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vanessa Bonke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oliver Musshoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marius Michels.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Michels, M., Bonke, V. & Musshoff, O. Understanding the adoption of smartphone apps in crop protection. Precision Agric 21, 1209–1226 (2020). https://doi.org/10.1007/s11119-020-09715-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-020-09715-5

Keywords

Search

Navigation