{"product_id":"big-data-in-bioeconomy-results-from-the-european-databio-project-9783030710712","title":"Big Data in Bioeconomy: Results from the European","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen.\u003c\/p\u003e\u003cp\u003eWith their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future.\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eWith its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eTerms, Acronyms and Abbreviations\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eForeword\u003c\/b\u003e  (PO or Director Data at European Commission- DG CONNECT) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eIntroduction to the book and the project \u0026amp; acknowledgements\u003c\/b\u003e (Editors, Thanasis) \u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart I – Technological Foundation: Big Data Technologies for BioIndustries  \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 1:\u003c\/b\u003e S\u003cb\u003etate of the art of technology and market potential\u003c\/b\u003e (Caj Södergård\/VTT\u003cb\u003e \u003c\/b\u003eet al)  \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 2: \u003c\/b\u003e\u003cb\u003eStandards\u003c\/b\u003e (Ingo Simonis\/OGC et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 3 : Sensor Data\u003c\/b\u003e (Savvas Rogotis\/NP et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 4: Geospatial Data \u003c\/b\u003e(Eva Klien \/Fraunhofer et al) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 5: Crowdsourced Data\u003c\/b\u003e (Karel Charvat\/Lespro)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 6 : Genomics Data \u003c\/b\u003e(Ephrem Habyarimana\u003cb\u003e \u003c\/b\u003e\/CREA et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 7:\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003cb\u003eIntegrating data sources with Linked \u003c\/b\u003e\u003cb\u003eData \u003c\/b\u003e(Christian Zinke-Wehlmann\/Infai et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 8: Linked Data usages in DataBio \u003c\/b\u003e(Christian Zinke-Wehlmann\/Infai et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 9:\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003cb\u003eData Pipelines: Modelling and Evaluation of\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003cb\u003emodels \u003c\/b\u003e(Kais Chaabouni)\/Softeam et al\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eChapter 10:\u003c\/b\u003e\u003cb\u003e Data Analytics and Machine Learning\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e(Pekka Siltanen\/VTT et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 11: \u003c\/b\u003e\u003cb\u003e Real Time Data Processing \u003c\/b\u003e(Fabiana Fournier\/IBM\u003cb\u003e, \u003c\/b\u003eet al) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 12:\u003c\/b\u003e\u003cb\u003e Privacy Preserving Analytics, Processing and Data Management\u003c\/b\u003e (Baldur Kubo\/Cybernetica)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 13:\u003c\/b\u003e\u003cb\u003e Data Visualisation\u003c\/b\u003e (Eva Klien\/Fraunhofer et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart II – Applications in Agriculture\u003c\/b\u003e               \u003c\/p\u003e  \u003cp\u003e\u003cb\u003e                Chapter 14:  \u003c\/b\u003e\u003cb\u003eWhat is Smart Agriculture \u003c\/b\u003e(Ephrem Habyarimana\/CREA, Christian Zinke-Wehlmann\/Infai)\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 15:\u003c\/b\u003e\u003cb\u003e NP’s Smart farming pilots\u003c\/b\u003e (Savvas Rogotis\/NP). \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 16:\u003c\/b\u003e\u003cb\u003e Big Data assets applied to the calculation of Irrigation needs in large scale Irrigation Communities\u003c\/b\u003e (Iluminada Sevilla\/Tragsa et al)\u003c\/p\u003e  \u003cp\u003e               \u003cb\u003eChapter 17:\u003c\/b\u003e\u003cb\u003e Genomics Biomass pilots\u003c\/b\u003e (Ephrem Habyarimana\/CREA et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 18: Yield estimation \u003c\/b\u003e\u003cb\u003ein Sorghum and Cultivated Potato\u003c\/b\u003e (Ephrem Habyarimana\/CREA, Nicole \u003c\/p\u003e  Bartelds\/NB Advies)\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 19:\u003c\/b\u003e \u003cb\u003eYield variability  mapping\u003c\/b\u003e (Karel Charvat, Lespro et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 20:\u003c\/b\u003e\u003cb\u003e Farm Weather Insurance Assessment\u003c\/b\u003e (Antonella Catucci\/e-Geos).  \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 21:\u003c\/b\u003e\u003cb\u003e Copernicus Data and CAP Subsidies Control \u003c\/b\u003e(Olimpia Copăcenaru\u003c\/p\u003e  \u003cp\u003e       \/Terrasigna et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 22: Future vision, Summary and Outlook\u003c\/b\u003e \u003c\/p\u003e   \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart III – Applications in Forestry\u003c\/b\u003e           \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 23: State of the art of technology and market potential\u003c\/b\u003e (Jukka  \u003c\/p\u003e  \u003cp\u003eMiettinen\/VTT)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 24:\u003c\/b\u003e\u003cb\u003e Finnish Forest Data based Metsään.fi-services\u003c\/b\u003e (Virpi Stenman\/Metsäkeskus) \u003c\/p\u003e  \u003cp\u003e               \u003cb\u003eChapter 25:\u003c\/b\u003e\u003cb\u003e Forest variable estimation and change monitoring by Big Data remote \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003e                               \u003c\/b\u003e\u003cb\u003esensing\u003c\/b\u003e ( Jukka Miettinen\/VTT et al) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 26:\u003c\/b\u003e\u003cb\u003e Monitoring Forest Health: Big Data applied to diseases and plagues control \u003c\/b\u003e(María Jose \u003c\/p\u003e  \u003cp\u003eCheca et al\/Tragsa)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 27:\u003c\/b\u003e\u003cb\u003e Forest damage monitoring for the bark beetle \u003c\/b\u003e(Petr Lukeš\/ FMI)                  \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 28: Future Vision, Summary of Big Data in Forestry \u003c\/b\u003e(Jukka Miettinen\/VTT et al)                        \u003c\/p\u003e  \u003cp\u003e                     \u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart IV – Applications in Fishery\u003c\/b\u003e              \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 29:\u003c\/b\u003e\u003cb\u003e State of the art of technology and market potential (\u003c\/b\u003eKarl-Johan Reite \/SINTEF et al) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 30: \u003c\/b\u003e\u003cb\u003eTuna Fisheries \u003c\/b\u003e(Iñaki Quincoces\/AZTI et al) \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 31: \u003c\/b\u003e\u003cb\u003eSmall pelagic fisheries \u003c\/b\u003e(K.G.A\/SINTEF et al)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 32: Future Vision, Summary and Outlook \u003c\/b\u003e(Josean Fernandes\/AZTI et al)\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePart V – Summary and Outlook \u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 33: Summary of Potential of Big Data Technology\u003c\/b\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 34: Outreach, Perspective, Exploitation\u003c\/b\u003e \u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChapter 35: How can I apply Big Data? \u003c\/b\u003e\u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default 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