@article{50b19ce3d3aa4a818017e8f22830b893,
title = "Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security",
abstract = "Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in {"}big data{"} analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.",
keywords = "3D crop modeling, Climate change, Scientific visualization",
author = "Christensen, {A. J.} and Venkatraman Srinivasan and Hart, {John C.} and Amy Marshall-Colon",
note = "Funding Information: Funding/support. This work was supported in part by grant ID 515760 from the Foundation for Food and Agriculture Research; by the Institute for Sustainability, Energy, and Environment (iSEE) at the University of Illinois at Urbana-Champaign; by the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign; by a Realizing Increased Photosynthetic Efficiency (RIPE) grant from the Bill and Melinda Gates Foundation; and by a Centrality of Advanced Digitally Enabled Science (CADENS) award ACI-1445176 from the National Science Foundation. Funding Information: This work was supported in part by grant ID 515760 from the Foundation for Food and Agriculture Research; by the Institute for Sustainability, Energy, and Environment (iSEE) at the University of Illinois at Urbana-Champaign; by the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign; by a Realizing Increased Photosynthetic Efficiency (RIPE) grant from the Bill and Melinda Gates Foundation; and by a Centrality of Advanced Digitally Enabled Science (CADENS) award ACI-1445176 from the National Science Foundation. The authors would like to thank Ms Rachel Shekar and Dr Matthew Turk for critically reading the manuscript and providing feedback. We thank Ms Kavya Kannan for help with the references. We also thank Dr Johnathan Lynch and Mr Xiyu Yang for supplying SimRoot images and data Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved.",
year = "2018",
month = may,
day = "1",
doi = "10.1093/nutrit/nux076",
language = "English (US)",
volume = "76",
pages = "332--347",
journal = "Nutrition reviews",
issn = "0029-6643",
publisher = "Wiley-Blackwell",
number = "5",
}