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RIM: Anatomy of a Weed Management Decision Support System for Adaptation and Wider Application

Published online by Cambridge University Press:  20 January 2017

Myrtille Lacoste  and
Stephen Powles
Myrtille Lacoste*
Affiliation:
Australian Herbicide Resistance Initiative (AHRI), School of Plant Biology, University of Western Australia, Perth WA 6009, Australia
Stephen Powles
Affiliation:
Australian Herbicide Resistance Initiative (AHRI), School of Plant Biology, University of Western Australia, Perth WA 6009, Australia
*
Corresponding author's E-mail: [email protected]
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Abstract

RIM, or “Ryegrass Integrated Management,” is a model-based software allowing users to conveniently test and compare the long-term performance and profitability of numerous ryegrass control options used in Australian cropping systems. As a user-friendly decision support system that can be used by farmers, advisers, and industry professionals, RIM can aid the delivery of key recommendations among the agricultural community for broadacre cropping systems threatened by herbicide resistance. This paper provides advanced users and future developers with the keys to modify the latest version of RIM in order to facilitate future updates, modifications, and adaptations to other situations. The various components of RIM are mapped and explained, and the key principles underlying the construction of the model are explained. The implementation of RIM into a Microsoft Excel® software format is also documented, with details on how user inputs are coded and parameterized. An overview of the biological, agronomic, and economic components of the model is provided, with emphasis on the ryegrass biological characteristics most critical for its effective management. The extreme variability of these parameters and the subsequent limits of RIM are discussed. The necessary compromises were achieved by emphasizing the primary end-use of the program as a decision support system for farmers and advisors.

Keywords

Annual rigid ryegrass, Lolium rigidum GaudAdoptionagriculturebio-economic modelsDSSextensionherbicide resistancemanagement simulationweed economics
Type
Weed Management
Information
Weed Science , Volume 63 , Issue 3 , September 2015 , pp. 676 - 689
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

AHRI Australian Herbicide Resistance Initiative (2013) RIM: Ryegrass Integrated Management–Australian Herbicide Resistance Initiative, The University of Western Australia. http://www.ahri.uwa.edu.au/RIM. Accessed August 15, 2014Google Scholar
Bagavathiannan, MV, Norsworthy, JK (2012) Late-season seed production in arable weed communities: management implications. Weed Sci. 60:325–334 Google Scholar
Bagavathiannan, MV, Norsworthy, JK, Lacoste, M, Powles, SB (2014) PAM: a decision support tool for guiding integrated management of palmer amaranth. in Proceedings of the Weed Science Society of America 2014 Conference, Vancouver. http://wssaabstracts.com/public/22/proceedings.html. Accessed August 15, 2014Google Scholar
Canner, SR, Wiles, LJ, Erskine, RH, McMaster, GS, Dunn, GH, Ascough, JC (2009) Modeling with limited data: the influence of crop rotation and management on weed communities and crop yield loss. Weed Sci. 57:175–186 Google Scholar
Chauhan, BS, Gill, G, Preston, C (2006) Influence of tillage systems on vertical distribution, seedling recruitment and persistence of rigid ryegrass (Lolium rigidum) seed bank. Weed Sci. 54:669–676 Google Scholar
Chauhan, BS, Gill, GS, Preston, C (2007) Effect of seeding systems and dinitroaniline herbicides on emergence and control of rigid ryegrass (Lolium Rigidum) in wheat. Weed Technol. 21:53–58 Google Scholar
Doole, GJ (2008) Increased cropping activity and herbicide resistance: the case of rigid ryegrass in Western Australian dryland agriculture. Pp 1–40 in Berklian, YU, ed. Crop Rotation: Economics, Impact, and Management. Hauppauge, NY Nova Science Publishers Google Scholar
Draper, AD, Roy, B (2002) Ryegrass RIM model stands the test of IWM field trial data. Pp 49–50 in Proceedings of AgriBusiness Crop Updates 2002. Perth, Australia Department of Agriculture and Food Western Australia Google Scholar
Goggin, DE, Powles, SB (2014). Fluridone: a combination germination stimulant and herbicide for problem fields? Pest Manag Sci. 70:1418–1424 DOI: 10.1002/ps.3721Google Scholar
Goggin, DE, Powles, SB, Steadman, KJ (2012) Understanding Lolium rigidum seeds: the key to managing a problem weed? Agronomy. 2:222–239 Google Scholar
Gonzalez-Andujar, JL, Fernandez-Quintanilla, C (2004) Modelling the population dynamics of annual ryegrass (Lolium rigidum) under various weed management systems. Crop Protection. 23:723–729 Google Scholar
Gonzalez-Andujar, JL, Fernandez-Quintanilla, C, Bastida, F, Calvo, R, Izquierdo, J, Lezaún, JA (2011) Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals. Weed Res. 513:304–309 Google Scholar
Hayman, PT (2004). Decision support systems in Australian dryland farming: a promising past, a disappointing present and uncertain future. In Proceeding for the 4th International Crop Science Congress. Brisbane, Australia Google Scholar
Hochman, Z, Carberry, PS (2011) Emerging consensus on desirable characteristics of tools to support farmers’ management of climate risk in Australia. Agricult Sys. 104:441–450 Google Scholar
Holst, N, Rasmussen, IA, Bastiaans, L (2007) Field weed population dynamics: a review of model approaches and applications. Weed Res. 47:1–14 Google Scholar
Lacoste, M (2013) RIM, Ryegrass Integrated Management–User guide. Australian Herbicide Resistance Initiative, The University of Western Australia, Perth. Pages 9 p. www.ahri.uwa.edu.au/RIM. Accessed August 15, 2014Google Scholar
Lacoste, M (2014) RIM 2013: default settings. Australian Herbicide Resistance Initiative and School of Agricultural and Resource Economics, The University of Western Australia, Perth. http://www.ahri.uwa.edu.au/RIM. Accessed August 15, 2014Google Scholar
Lacoste, M, Powles, SB (2014) Upgrading the RIM Model for improved support of integrated weed management extension efforts in cropping systems. Weed Technol. 28:703–720 Google Scholar
Lawes, R, Renton, M (2010) The Land Use Sequence Optimiser (LUSO): a theoretical framework for analysing crop sequences in response to nitrogen, disease and weed populations. Crop Past Sci. 61:835–843 Google Scholar
Llewellyn, RS, D'Emden, FH, Kuehnea, G (2012) Extensive use of no-tillage in grain growing regions of Australia. Field Crop Res. 132:204–212 Google Scholar
Michael, PJ, Owen, MJ, Powles, SB (2010) Herbicide-resistant weed seeds contaminate grain sown in the Western Australian grainbelt. Weed Sci. 58:466–472 Google Scholar
Monjardino, M, Pannell, DJ, Powles, SB (2003) Multispecies resistance and integrated management: a bioeconomic model for integrated management of rigid ryegrass (Lolium rigidum) and wild radish (Raphanus raphanistrum). Weed Sci. 51:798–809 Google Scholar
Monjardino, M, Pannell, DJ, Powles, SB (2005) The economic value of glyphosate-resistant canola in the management of two widespread crop weeds in a Western Australian farming system. Agric Sys. 84:297–315 Google Scholar
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvold, G, Powles, SB, Burgos, NR, Witt, WW, Barrett, M (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci. 60:31–62 Google Scholar
Pannell, D, Stewart, V, Bennett, A, Monjardino, M, Schmidt, C, Draper, A, Powles, SB (2004a) RIM 2004–User's manual. Crawley, Australia The University of Western Australia. 49 pGoogle Scholar
Pannell, DJ, Stewart, V, Bennett, A, Monjardino, M, Schmidt, C, Powles, SB (2004b) RIM: a bioeconomic model for integrated weed management of Lolium rigidum in Western Australia. Agric Sys. 79:305–325 Google Scholar
Pluske, JM, Pannell, DJ, Bennett, AL (2004) RIM 2004 Reference Manual. A Decision Tool for Integrated Management of Herbicide-Resistant Annual Ryegrass. Crawley, Australia School of Agricultural and Resource Economics, University of Western Australia. 46 pGoogle Scholar
Stanton, RA, Pratley, JE, Hudson, D, Dill, GM (2008) A risk calculator for glyphosate resistance in Lolium rigidum (Gaud). Pest Manag Sci. 64:402–408 Google Scholar
Steadman, KJ, Easton, DM, Plummer, JA, Ferris, DG, Powles, SB (2006) Late-season non-selective herbicide application reduces Lolium rigidum seed numbers, seed viability, and seedling fitness. Aust J Exp Agric. 57:133–141 Google Scholar
Walkenback, J (2010) Excel® 2010 Power Programming with VBA. Hoboken, NJ Wiley Publishing, Inc. 1052 pGoogle Scholar
Walsh, MJ, Powles, SB (2007) Management strategies for herbicide-resistant weed populations in Australian dryland crop production systems. Weed Technol. 21:332–338 Google Scholar
Walsh, M, Newman, P, Powles, S (2013) Targeting weed seeds in-crop: a new weed control paradigm for global agriculture. Weed Technol. 27:431–436 Google Scholar
Werth, J, Thornby, D, Walker, S (2011) Assessing weeds at risk of evolving glyphosate resistance in Australian sub-tropical glyphosate-resistant cotton systems. Crop Past Sci. 62:1002–1009 Google Scholar