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Springer Decision Support Systems for Weed Management

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Description

Weed management Decision Support Systems (DSS) are increasingly important computer-based tools for modern agriculture. Nowadays, extensive agriculture has become highly dependent on external inputs and both economic costs, as well the negative environmental impact of agricultural activities, demands knowledge-based technology for the optimization and protection of non-renewable resources. In this context, weed management strategies should aim to maximize economic profit by preserving and enhancing agricultural systems. Although previous contributions focusing on weed biology and weed management provide valuable insight on many aspects of weed species ecology and practical guides for weed control, no attempts have been made to highlight the forthcoming importance of DSS in weed management.  This book is a first attempt to integrate `concepts and practice’ providing a novel guide to the state-of-art of DSS and the future prospects which hopefully would be of interest to higher-level students, academics and professionals in related areas. From the Back Cover Weed management Decision Support Systems (DSS) are increasingly important computer-based tools for modern agriculture. Nowadays, extensive agriculture has become highly dependent on external inputs and both economic costs, as well the negative environmental impact of agricultural activities, demands knowledge-based technology for the optimization and protection of non-renewable resources. In this context, weed management strategies should aim to maximize economic profit by preserving and enhancing agricultural systems. Although previous contributions focusing on weed biology and weed management provide valuable insight on many aspects of weed species ecology and practical guides for weed control, no attempts have been made to highlight the forthcoming importance of DSS in weed management.  This book is a first attempt to integrate 'concepts and practice' providing a novel guide to the state-of-art of DSS and the future prospects which hopefully would be of interest to higher-level students, academics and professionals in related areas. About the Author Guillermo R. Chantre is an Associate Professor and Researcher in Weed Ecology at the Department of Agronomy of Universidad Nacional del Sur and CERZOS/CONICET (National Council of Scientific and Technological Research of Argentina). He specializes in weed ecophysiology, weed management and agricultural decision support systems. He is currently a member of the Advisory Board of the Socio-Technological Development Area for postgraduate scholarship grants at the National Council of Scientific and Technological Research of Argentina. He is also an advisor for technological scientific projects on agricultural systems and also serves as an external reviewer for several SCI journals. Jose Luis Gonzalez-Andujar is a Senior Researcher at the Spanish Research Council (CSIC) and has research interests in weed ecology and weed management. Currently he is the leader of the Weed Agroecology group and Scientific Director of the International Laboratory on Global Change (LINCGlobal). He is a member of the editorial boards of Weed Research, PLOS ONE and Agronomia y Ambiente, having served in the past as Associated Editor of Communications in Biometry and Crop Science, member of the editorial advisory committees of Biometrics, Biometry Bulletin and Journal of Agricultural, Biological and Environmental Statistics. He was also President of the Spanish Weed Science Society, President and Past-President of the Latin-American Association of Weed Science, President and Vice-President of the Spanish Biometric Society and President of the Spanish Region of the International Biometric Society, Vice-president and Secretary of the Spanish Society of Information Technology in Agriculture and Forestry. He has received national and international awards and recognitions, including

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
01 August 2020
Listed Since
26 February 2020

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