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Accepted manuscript

Development and Validation of Avena Integrated Management (AIM): A Bioeconomic Decision Support Tool for Wild Oat Management in Australian Grain Production Systems

Published online by Cambridge University Press:  23 May 2024

David Thornby Open the ORCID record for David Thornby [Opens in a new window] ,
Caleb C. Squires  and
Michael J. Walsh Open the ORCID record for Michael J. Walsh [Opens in a new window]
David Thornby
Affiliation:
Research Consultant, Innokas Intellectual Services, Trafalgar, VIC 3824, Australia;
Caleb C. Squires
Affiliation:
Postdoctoral Research Associate, University of Sydney, Brownlow Hill, NSW 2570, Australia;
Michael J. Walsh*
Affiliation:
Associate Professor, University of Sydney, Brownlow Hill, NSW 2570, Australia.
*
Author for correspondence: Michael J. Walsh, Associate Professor, University of Sydney, Brownlow Hill, NSW 2570, Australia., (Email: m.j.walsh@sydney.edu.au)
Rights & Permissions [Opens in a new window]

Abstract

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Wild oat is a long-standing weed problem in Australian grain cropping systems, potentially reducing the yield and quality of winter grain crops significantly. The effective management of wild oat requires an integrated approach of diverse control techniques that suit specific crops and cropping situations. This research aimed to construct and validate a bioeconomic model that enables the simulation and integration of weed control technologies for wild oat in grain production systems. The Avena spp. integrated management (AIM) model was developed with a simple interface to provide outputs of biological and economic data (crop yields, weed control costs, emerged weeds, weed seedbank, gross margins) on wild oat management data in a cropping rotation. Uniquely, the AIM was validated against real-world data on wild oat management in a wheat and sorghum cropping rotation, where the model was able to reproduce the patterns of wild oat population changes as influenced by weed control and agronomic practices. Correlation coefficients for 12 comparison scenarios ranged between 0.55 and 0.96. With accurate parameterization, AIM is thus able to make useful predictions on the effectiveness of individual and integrated weed management tactics for wild oat control in grain cropping systems.

Keywords

decision support systemsweed managementmodeling
Type
Research Article
Information
Weed Technology , Accepted manuscript , pp. 1 - 33
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© Weed Science Society of America, 2024