Big Data in Bioeconomy - Results from the European DataBio Project (Paperback, 1st ed. 2021)

This 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. With 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. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	September 2021
First published:	2021
Editors:	Caj Soedergard • Tomas Mildorf • Ephrem Habyarimana • Arne J. Berre • Jose A. Fernandes • Christian Zinke-Wehlmann
Dimensions:	235 x 155 x 27mm (L x W x T)
Format:	Paperback
Pages:	423
Edition:	1st ed. 2021
ISBN-13:	978-3-03-071071-2
Categories:	Books > Professional & Technical > Agriculture & farming > Agricultural science Books > Professional & Technical > Agriculture & farming > Forestry & silviculture: practice & techniques Books > Earth & environment > The environment > Environmental economics > General Books > Computing & IT > Applications of computing > Databases > General
LSN:	3-03-071071-8
Barcode:	9783030710712

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