Integrated description of agricultural field experiments and production: the ICASA version 2.0 data standards

Authors: White, Jeffrey; HUNT, L - University Of Guelph; BOOTE, KENNETH - University Of Florida; JONE, JAMES - University Of Florida; KOO, JAWOO - International Food Policy Researc Institute (IFPRI); SOONHO, KIM - International Food Policy Researc Institute (IFPRI); PORTER, CHERYL - University Of Florida; WILKINS, PAUL - International Fertilizer Development Center (IFDC); HOOGENBOOM, GERRIT - Washington State University

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2013
Publication Date: 5/20/2013
Citation: White, J.W., Hunt, L.A., Boote, K.J., Jone, J.W., Koo, J., Soonho, K., Porter, C., Wilkins, P.W., Hoogenboom, G. (2013). Integrated description of agricultural field experiments and production: the ICASA version 2.0 data standards. Computers and Electronics in Agriculture, 96:1-12.

Interpretive Summary: Agricultural research increasingly seeks to provide quantitative information on how different factors such as water and nitrogen use affect the environment and how crops may respond to future environmental conditions as affected by climate change or other processes. This goal often leads to investigation where multiple sets of experimental data are examined using computer models and pther powerful numerical tools. The use of standard data formats for documenting experiments and modeling crop growth and development may sharing of information and software easier, allowing researchers to focus on their research problems rather than on converting and re-formatting data. Standards for describing field experiments were developed by the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project and subsequently, by the International Consortium for Agricultural Systems Applications (ICASA). These greatly aided data interchange but were incomplete and contained inconsistencies. The ICASA standards have evolved over the past 10 years, emphasizing standardizing vocabularies, clarifying relations among variables, and expanding formats beyond the original plain text file format. This paper provides an overview of the ICASA Version 2.0 standards. The plain text implementation is described in detail. Implementations in relational databases, spreadsheets and other formats are also considered. Areas for further improvement and development are noted, particularly as related to pest damage, data quality and appropriate use of datasets. Continued refinement and development of the standards should increase research efficiency, ultimately leading to better information for producers, consumers and other stake-holders in agriculture.

Technical Abstract: Agricultural research increasingly seeks to quantify complex interactions of processes for a wide range of environmental conditions and crop management scenarios, leading to investigation where multiple sets of experimental data are examined using tools such as simulation and regression. The use of standard data formats for documenting experiments and modeling crop growth and development facilitates exchange of information and software, allowing researchers to focus on research per se rather than on converting and re-formatting data. The standards developed by the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project and subsequently, by the International Consortium for Agricultural Systems Applications (ICASA), were of considerable value but were incomplete and contained inconsistencies. The ICASA standards have evolved over the past 10 years, emphasizing standardizing vocabularies, clarifying relations among variables, and expanding formats beyond the original plain text file format. This paper provides an overview of the ICASA Version 2.0 standards. The plain text implementation is described in detail. Implementations in relational databases, spreadsheets and the eXtended Markup Language (XML) are also considered. Areas for further improvement and development are noted, particularly as related to pest damage, data quality and appropriate use of datasets.