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Next generation agricultural system data, models and knowledge products: Introduction
Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Applied Science [etc.]
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485643/ https://www.ncbi.nlm.nih.gov/pubmed/28701811 http://dx.doi.org/10.1016/j.agsy.2016.09.003 |
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author | Antle, John M. Jones, James W. Rosenzweig, Cynthia E. |
author_facet | Antle, John M. Jones, James W. Rosenzweig, Cynthia E. |
author_sort | Antle, John M. |
collection | PubMed |
description | Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30–40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a “NextGen” study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation. |
format | Online Article Text |
id | pubmed-5485643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Elsevier Applied Science [etc.] |
record_format | MEDLINE/PubMed |
spelling | pubmed-54856432017-07-10 Next generation agricultural system data, models and knowledge products: Introduction Antle, John M. Jones, James W. Rosenzweig, Cynthia E. Agric Syst Article Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30–40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a “NextGen” study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation. Elsevier Applied Science [etc.] 2017-07 /pmc/articles/PMC5485643/ /pubmed/28701811 http://dx.doi.org/10.1016/j.agsy.2016.09.003 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Antle, John M. Jones, James W. Rosenzweig, Cynthia E. Next generation agricultural system data, models and knowledge products: Introduction |
title | Next generation agricultural system data, models and knowledge products: Introduction |
title_full | Next generation agricultural system data, models and knowledge products: Introduction |
title_fullStr | Next generation agricultural system data, models and knowledge products: Introduction |
title_full_unstemmed | Next generation agricultural system data, models and knowledge products: Introduction |
title_short | Next generation agricultural system data, models and knowledge products: Introduction |
title_sort | next generation agricultural system data, models and knowledge products: introduction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5485643/ https://www.ncbi.nlm.nih.gov/pubmed/28701811 http://dx.doi.org/10.1016/j.agsy.2016.09.003 |
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