Smartphone adoption and use in agriculture: empirical evidence from Germany

Michels, Marius; Fecke, Wilm; Feil, Jan‐Henning; Mußhoff, Oliver; Pigisch, Johanna; Krone, Saskia

doi:10.1007/s11119-019-09675-5

Cited by 112 publications

(99 citation statements)

References 78 publications

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“…2018; Michels et al . 2020). Baumüller (2012) pointed out that the use of mobile phones can facilitate agricultural technology adoption among farmers in developing countries because it helps overcome some of the obstacles to technology adoption by facilitating access to information and learning, financial services, and input and output markets.…”

Section: Theoretical Framework and Econometric Approachmentioning

confidence: 99%

“…It is rational to use the household head's smartphone use status as an instrumental variable. The existing literature has shown that the use of information and communication technologies such as mobile phones/smartphones facilitates agricultural technology adoption (Baumüller 2012;Aker and Ksoll 2016;Maredia et al 2018;Michels et al 2020). Baumüller (2012) pointed out that the use of mobile phones can facilitate agricultural technology adoption among farmers in developing countries because it helps overcome some of the obstacles to technology adoption by facilitating access to information and learning, financial services, and input and output markets.…”

Section: First-stage Analysis: Modelling the Decision Of Farm Machinementioning

confidence: 99%

See 1 more Smart Citation

Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

Zhou

et al. 2020

Aus J Agri & Res Econ

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Crop production in developing and emerging countries is increasingly dependent on the usage of farm machinery. However, it remains unclear whether low-productive and high-productive farmers benefit equally from farm machinery use. To address the research gap, this study examines the potential heterogeneous effects of farm machinery use on maize yields, using an unconditional quantile regression model and survey data from China. We employ a control function approach to address the selection bias issue associated with farm machinery use. The empirical results show that the use of farm machinery significant increases maize yields for all the selected quantiles (except for the 80th quantile); the low-productive farmers tend to benefit more from farm machinery use relative to their high-productive counterparts; and farm machinery use reduces the inequality and variability of maize yields.

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Section: Theoretical Framework and Econometric Approachmentioning

confidence: 99%

Section: First-stage Analysis: Modelling the Decision Of Farm Machinementioning

confidence: 99%

Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

Zhou

et al. 2020

Aus J Agri & Res Econ

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“…Apps for data retrieval and citizen sciences increased dramatically in recent years [20]. Most smartphones have built-in GPS receivers, cameras, and microphones [21].…”

Section: Related Workmentioning

confidence: 99%

GeoFairy2: A Cross-Institution Mobile Gateway to Location-Linked Data for In-Situ Decision Making

Sun

Cvetojevic

et al. 2020

IJGI

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To effectively disseminate location-linked information despite the existence of digital walls across institutions, this study developed a cross-institution mobile App, named GeoFairy2, to overcome the virtual gaps among multi-source datasets and aid the general users to make thorough accurate in-situ decisions. The app provides a one-stop service with relevant information to assist with instant decision making. It was tested and proven to be capable of on-demand coupling and delivering location-based information from multiple sources. The app can help general users to crack down the digital walls among information pools and serve as a one-stop retrieval place for all information. GeoFairy2 was experimented with to gather real-time and historical information about crops, soil, water, and climate. Instead of a one-way data portal, GeoFairy2 allows general users to submit photos and observations to support citizen science projects and derive new insights, and further refine the future service. The two-directional mechanism makes GeoFairy2 a useful mobile gateway to access and contribute to the rapidly growing, heterogeneous, multisource, and location-linked datasets, and pave a way to drive us into a new mobile web with more links and less digital walls across data providers and institutions.

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“…For example, the relevance of the role of farm attributes and socio-demographic characteristics of farmers, such as education or age have been investigated [ 28 , 29 ]. With regard to smart products, only the use and acceptance of smartphones among farmers has been studied in Germany to date [ 30 ]. Further empirical studies explicitly investigating the acceptance of smart products in German livestock farming are non-existent.…”

Section: Introductionmentioning

confidence: 99%

Smart Products in Livestock Farming—An Empirical Study on the Attitudes of German Farmers

Schukat

Heise

2021

Animals

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In recent years, the fourth industrial revolution has found its way into agriculture. Under the term smart farming, various so-called smart products are offered that may positively influence both the daily work of farmers and animal welfare. These smart products can collect data from the farm, extract important information, and in some cases even make decisions independently. Particularly in Germany, where intensive livestock farming is criticized by society, such smart products could make a significant contribution to improving animal welfare. However, an important prerequisite is the acceptance of the users, who are usually the livestock farmers themselves. So far, there is little knowledge about farmers’ attitudes towards smart products in livestock production. In this study, a factor analysis and a cluster analysis are conducted to evaluate the attitudes of German livestock farmers towards smart products. Based on the analysis of an online questionnaire in which German livestock farmers (n = 422) participated, four clusters could be derived. The main distinguishing characteristics of the clusters are the influence of the social environment, the expected effort for implementation, the general trust in smart products, and the technology readiness of the farms. As a result, this study provides valuable insights for technology providers of smart products for livestock farming as well as for policy makers.

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Smartphone adoption and use in agriculture: empirical evidence from Germany

Cited by 112 publications

References 78 publications

Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

GeoFairy2: A Cross-Institution Mobile Gateway to Location-Linked Data for In-Situ Decision Making

Smart Products in Livestock Farming—An Empirical Study on the Attitudes of German Farmers

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