Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-24T01:51:10.953Z Has data issue: false hasContentIssue false

WeedML: A Tool for Collaborative Weed Demographic Modeling

Published online by Cambridge University Press:  20 January 2017

Niels Holst*
Affiliation:
Aarhus University, Faculty of Agricultural Sciences, Department of Integrated Pest Management, Flakkebjerg, 4200 Slagelse, Denmark
*
Corresponding author's E-mail: [email protected]

Abstract

WeedML is a proposed standard to formulate models of weed demography (or perhaps even complex models in general) that are both transparent and straightforward to reuse as building blocks for new models. The paper describes the design and thoughts behind WeedML which relies on XML and object-oriented systems development. Proof-of-concept software is provided as open-source C++ code and executables that can be downloaded freely.

Type
Symposium
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Cousens, R. D. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.Google Scholar
Cousens, R. D. 1999. Weed science doesn't have to be a contradiction in terms. Pages. 364373. in. Twelfth Australian Weeds Conference. Hobart, Tasmania Tasmanian Weed Society.Google Scholar
Fogel, K. 2003. Open Source Development with CVS. 3rd ed. Scottsdale, AZ Paraglyph. 342 p.Google Scholar
Freckleton, R. P., Sutherland, W. J., Watkinson, A. R., and Stephens, P. A. 2008. Modelling the effects of management on population dynamics: some lessons from annual weeds. J. Appl. Ecol. 45:10501058.Google Scholar
Holst, N. 2005. Recursive density equivalents: an improved method for forecasting yield loss caused by mixed weed populations. J. Agric. Sci. 143:293298.Google Scholar
Holst, N., Rasmussen, I. A., and Bastiaans, L. 2007. Field weed population dynamics: a review of model approaches and applications. Weed Res. 47:114.CrossRefGoogle Scholar
Hucka, M., Finney, A., Sauro, H. M., et al. 2003. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics (Oxford) 19:524531.CrossRefGoogle ScholarPubMed
Kay, M. 2004. XPath 2.0 Programmer's Reference. Indianapolis, IN Wiley Publishing, Inc. 530 p.Google Scholar
Larkin, T. S. and Carruthers, R. I. 1990. Development of a hierarchical simulation environment for research biologists. Pages 4954. In Gausch, A. ed. Object Oriented Simulation Proceedings of the SCS Multiconference on Object Oriented Simulation.Google Scholar
Larkin, T. S., Carruthers, R. I., and Legaspi, B. C. 2000. Two-dimensional distributed delays for simulating two competing biological processes. Trans. Soc. Comp. Simul. Internat. 17:2533.Google Scholar
Martin, R. C. 2006. Agile Software Development: Principles, Patterns, and Practices. Upper Saddle River, NJ Prentice Hall. 529 p.Google Scholar
Moss, S. R. 2008. Weed research: is it delivering what it should? Weed Res. 48:389393.CrossRefGoogle Scholar
Stroustrup, B. 1997. The C++ Programming Language. 3rd ed. Reading, MA Addison-Wesley. 920 p.Google Scholar