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Smartphone adoption and use in agriculture: empirical evidence from Germany

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Abstract

Smartphone technology is promising for the future development of agriculture, as it can facilitate and improve many operational procedures and can also be combined with precision agriculture technologies. Yet, existing research on smartphone adoption in agriculture is scarce. Therefore, this paper empirically explores the factors influencing smartphone adoption by German farmers. The relationship between farmers, farm characteristics and smartphone adoption was analysed using a binomial logit model. The dataset, collected in 2016, consists of 817 German farmers and is representative in terms of age, farm size and diversification as well as regional distribution across the study area. The results indicate that, among other factors, farmers’ age, education, and farm size are determinants of smartphone adoption. Furthermore, the paper provides descriptive information about the usage of smartphone functions and agriculture-related app functions. Thus, this paper contributes to the literature by identifying key determinants of smartphone adoption in agriculture. The findings may be of interest for policy makers, researchers in the field of precision agriculture technologies as well as developers and providers of farm equipment and precision agriculture technologies that integrate with smartphones, since the paper includes information concerning smartphone use and key factors influencing smartphone adoption.

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Notes

  1. For an overview see Dedehayir et al. (2017).

  2. It should be noted that there also studies that find a positive, statistically significant effect (Isgin et al. 2008) or no statistically significant effect (Daberkow and McBride 2003) of age on PAT adoption.

  3. The reader should be cautioned that the number of female farmers in the sample is small which could limit the resilience of the statistical results.

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Michels, M., Fecke, W., Feil, JH. et al. Smartphone adoption and use in agriculture: empirical evidence from Germany. Precision Agric 21, 403–425 (2020). https://doi.org/10.1007/s11119-019-09675-5

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