Business modelling for a digital compliance platform: taking stock and looking forward : (D3.2.1 Desk study and interviews)

Abstract

, 2017. Business modelling for a digital compliance platform: Taking stock and looking forward; (D3.2.1 Desk study and interviews).Wageningen, Wageningen Economic Research, Report 2017-014. 38 pp.; 9 fig.; 2 tab.; 23 ref. This report presents findings from a desk study and the first round of stakeholder consultation on the business models of Ag data platforms that are relevant to developing a compliance platform. It assembles possible analogs and antilogs that can be used to gauge the options for the compliance platform as envisaged by FarmDigital.Key words: data platform, business model, value network, digital compliance This report can be downloaded for free at http://dx.doi.org/10.18174/406002 or at www.wur.eu/economic-research (under Wageningen Economic Research publications). The user may reproduce, distribute and share this work and make derivative works from it. Material by third parties which is used in the work and which are subject to intellectual property rights may not be used without prior permission from the relevant third party. The user must attribute the work by stating the name indicated by the author or licensor but may not do this in such a way as to create the impression that the author/licensor endorses the use of the work or the work of the user. The user may not use the work for commercial purposes.Wageningen Economic Research accepts no liability for any damage resulting from the use of the results of this study or the application of the advice contained in it. Key issues to be addressed in business modelling of the compliance platform are the following:• The level of collectiveness or openness in ownership structure and platform development• The value creation mechanism through data and information exchange• Monetarisation strategy• Launching strategy• Governance. S.2 MethodologyA wide range of business models are used for developing Ag data platforms in agrifood chains.This report presents findings from a desk study and the first round of stakeholder consultation on the 1 Introduction BackgroundAs digitalisation of farming processes continues to expand and intensify, the supply of and the demand for farming data are rapidly growing (Sonka, 2014;Zhang and Shen, 2011). Farm data are generated by a variety of farming operations, ranging from financial and production records traditionally kept by farm management to machine-generated data coming from sensors and smart farm equipment.Demand for farming data is on the one hand driven by the need to make informed decisions and on the other hand pulled by informational institutions such as standards and certification schemes for governance purposes such as transparency and sustainability (Ge and Brewster, 2016;Verbeke, 2005).To meet the demand for information and to prove compliance with relevant requirements, farmers in the agrifood chains must record and report all kinds of data. The majority of this agriculture-related data are still paper-based, spread over different systems and difficult to exchange between interested parties. There is ...