Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications

A - Papers appearing in refereed journals

Bagavathiannan, M. V., Beckie, H. J., Chantre, G. R., Gonzalez-Andujar, J. L., Leon, R. G., Neve, P., Poggio, S. L., Schutte, B. J., Somerville, G., Werle, R. and Van Acker, R. 2020. Simulation Models on the Ecology and Management of Arable Weeds: Structure, Quantitative Insights, and Applications. Agronomy. 10, p. 1611. https://doi.org/10.3390/agronomy10101611

AuthorsBagavathiannan, M. V., Beckie, H. J., Chantre, G. R., Gonzalez-Andujar, J. L., Leon, R. G., Neve, P., Poggio, S. L., Schutte, B. J., Somerville, G., Werle, R. and Van Acker, R.
Abstract

In weed science and management, models are important and can be used to betterunderstand what has occurred in management scenarios, to predict what will happen and to evaluatethe outcomes of control methods. To-date, perspectives on and the understanding of weed modelshave been disjointed, especially in terms of how they have been applied to advance weed scienceand management. This paper presents a general overview of the nature and application of a fullrange of simulation models on the ecology, biology, and management of arable weeds, and howthey have been used to provide insights and directions for decision making when long-term weedpopulation trajectories are impractical to be determined using field experimentation. While researchon weed biology and ecology has gained momentum over the past four decades, especially for specieswith high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycleparameters for many agriculturally important weed species. More research efforts should be investedin filling these knowledge gaps, which will lead to better models and ultimately better inform weedmanagement decision making

KeywordsWeed seedling emergence; Geneflow; Crop-weed competition; Weed population dynamics; Herbicide resistance; Decision-support tools; Predictive models
Year of Publication2020
JournalAgronomy
Journal citation10, p. 1611
Digital Object Identifier (DOI)https://doi.org/10.3390/agronomy10101611
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Global Challenges Research Fund (UKRI)
Funder project or codeBB/S014683/1
Publisher's version
Output statusPublished
Publication dates
Online21 Oct 2020
Publication process dates
Accepted14 Oct 2020
ISSN2073-4395
PublisherMDPI

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