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25 October 2024 Development and validation of Avena integrated management (AIM): a bioeconomic decision support tool for wild oat management in Australian grain production systems
David Thornby, Caleb C. Squires, Michael J. Walsh
Author Affiliations +
Abstract

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 comprising 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, 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 of the effectiveness of individual and integrated weed management tactics for wild oat control in grain cropping systems.

Nomenclature: Wild oat; Avena fatua L.; A. sterilis L. ssp. ludoviciana (Durieu) Gillet & Magne; sorghum; Sorghum bicolor (L.) Moench; wheat; Triticum aestivum L.

David Thornby, Caleb C. Squires, and Michael J. Walsh "Development and validation of Avena integrated management (AIM): a bioeconomic decision support tool for wild oat management in Australian grain production systems," Weed Technology 38(1), 1-14, (25 October 2024). https://doi.org/10.1017/wet.2024.35
Received: 19 February 2024; Accepted: 15 May 2024; Published: 25 October 2024
KEYWORDS
decision support systems
modeling
weed management
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