Socio-economic impact of Big Data and Smart Farming

Socio-economic impact of Big Data and Smart Farming

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  Socio-economic impact ofBig Data and Smart Farming Sjaak Wolfert – Sr. Scientist Infomanagement & ICT in Agri-Food Credits: Lan Ge, Cor Verdouw & Marc-Jeroen Bogaardt Studium Generale, Van Hall Larenstein, Leeuwarden, 11 May 2016
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  Take-home messages  Agri-Foodchains become more technology/data-driven ● Can cause major shifts in roles and power relations among different players in agri-food chain networks ● Infrastructure and software development are key issues  Significant socio-economic impacts; two scenarios: 1. Strong integrated supply chain • farmer becomes franchiser/contractor • limited freedom in doing business 2. Open collaboration network • Farmer empowered through easier switch between suppliers • Options for direct sales to consumers Reality somewhere in between? F F
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  Disruptive ICT Trends Mobile/Cloud Computing – smart phones, wearables, incl. sensors  Location-based monitoring - satellite and remote sensing technology, geo information, drones, etc.  Social media - Facebook, Twitter, Wiki, etc.  Internet of Things – everything gets connected in the internet (virtualisation, M2M, autonomous devices) Big Data - Web of Data, Linked Open Data High Potential for unprecedented innovations! everywhere anything anywhere everybody everything
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  smart sensing & monitoring smartanalysis & planning smart control Closing the cyber-physical management cycle BIG DATA
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  Big Data involvesthe whole supply chain network and beyond 5 Source: Hisense.com Smart Farming Smart Logistics tracking/& tracing Domotics Health Fitness/Well-being
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  Challenges of BigData in Smart Farming  Big data is more about identifying the right questions instead of finding the right answers  The importance of analytics (intelligence) ● ‘Actionable data’ ● Integration of various data sources (intelligent processing). ● Linking ‘small data’ systems to the application of big data  Addressing societal issues ● Privacy and data ownership ● Supply chain organization ● Business models – sharing costs and revenues
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  Redefining Industry Boundaries(1/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 7 3. Smart, connected product + + + 2. Smart Product 1. Product
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  Redefining Industry Boundaries(2/2) (according to Porter and Heppelmann, Harvard Business Review, 2014) 8 5. System of systems farm management system farm equipment system weather data system irrigation system seed optimizing system field sensors irrigation nodes irrigation application seed optimization application farm performance database seed database weather data application weather forecasts weather maps rain, humidity, temperature sensors farm equipment system planters tillers combine harvesters 4. Product system Your company How many platforms should users and developers enter? How many interfaces to maintain?
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  Battlefield of BigData & Smart Farming Farm Farm Farm Farm Data Start-ups Farming Cooperatives Open Ag Data Alliance ... AgBusiness Monsanto Cargill Dupont ... Tech Companies Google IBM Oracle ... Ag Tech John Deere Trimble Precision planting ... Tech Start-upsFarm Tech Start-ups Data Start-upsVenture Capital Anterra Founders Fund Kleiner Perkins ... Farm
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  The USA battleground:Monsanto (et al.) 10 PRESCRIPTIVE FARMING based on VARIABLE RATE APPLICATION
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  Dairy Software Ecosystem Data-drivendairy application development Genotypic cow data Roughage intake Medicines Milk production Animal monitoring Logistics Dairy products/ process Consumer use Open Data Infrastructure (privacy, security, trust) Application Services & Components Platform Actors Open Software Organization Domain Knowledge/ Models Concentra tes intake ...? ...?
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  New Business Modelsbased on Big Data See: Arent van 't Spijker: "The New Oil - using innovative business models to turn data into profit“, 2014  Basic data sales ● commercial equivalent of open data (e.g. FarmMobile)  Product innovation ● use data to improve your product (machinery industry, e.g. John Deere, Lely’s milking robots)  Commodity swap ● data for data (e.g. between farmers and (food) processors to increase service component)  Value chain integration ● use data to control the whole chain (e.g. Monsanto’s Fieldscript)  Value net creation ● pool data from the same consumer (e.g. AgriPlace)
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  Take-home messages  Agri-Foodchains become more technology/data-driven ● Can cause major shifts in roles and power relations among different players in agri-food chain networks ● Infrastructure and software development are key issues  Significant socio-economic impacts; two scenarios: 1. Strong integrated supply chain • farmer becomes franchiser/contractor • limited freedom in doing business 2. Open collaboration network • Farmer empowered through easier switch between suppliers • Options for direct sales to consumers Reality somewhere in between? F F
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  Thank you for yourattention Questions? More information [email protected] nl.linkedin.com/in/sjaakwolfert/ Twitter: @sjaakwolfert http://www.slideshare.net/SjaakWolfert