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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. Lastly, 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.

Authors:
 [1];  [2];  [3];  [4]
  1. Univ. of Illinois, Urbana-Champaign, IL (United States). National Center for Supercomputing Applications (NCSA)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Univ. of Illinois, Urbana-Champaign, IL (United States). Dept. of Computer Science
  4. Univ. of Illinois, Urbana-Champaign, IL (United States). Dept. of Plant Biology
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; National Science Foundation (NSF); Bill and Melinda Gates Foundation; Foundation for Food and Agriculture Research (FFAR)
OSTI Identifier:
1438248
Grant/Contract Number:  
AC05-76RL01830; 515760
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nutrition Reviews
Additional Journal Information:
Journal Volume: 76; Journal Issue: 5; Journal ID: ISSN 0029-6643
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 60 APPLIED LIFE SCIENCES; climate change; scientific visualization; 3D crop modeling

Citation Formats

Christensen, A. J., Srinivasan, V., Hart, J. C., and Marshall-Colon, A. Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security. United States: N. p., 2018. Web. doi:10.1093/nutrit/nux076.
Christensen, A. J., Srinivasan, V., Hart, J. C., & Marshall-Colon, A. Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security. United States. doi:10.1093/nutrit/nux076.
Christensen, A. J., Srinivasan, V., Hart, J. C., and Marshall-Colon, A. Sat . "Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security". United States. doi:10.1093/nutrit/nux076. https://www.osti.gov/servlets/purl/1438248.
@article{osti_1438248,
title = {Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security},
author = {Christensen, A. J. and Srinivasan, V. and Hart, J. C. and Marshall-Colon, A.},
abstractNote = {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. Lastly, 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.},
doi = {10.1093/nutrit/nux076},
journal = {Nutrition Reviews},
number = 5,
volume = 76,
place = {United States},
year = {Sat Mar 17 00:00:00 EDT 2018},
month = {Sat Mar 17 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
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