Abstract
Concerns around herbicide resistance, human risk, and the environmental impacts of current weed control strategies have led to an increasing demand for alternative weed management methods. Many new weed management strategies are under development; however, the poor availability of accurate weed maps, and a lack of confidence in the outcomes of alternative weed management strategies, has hindered their adoption. Developments in field sampling and processing, combined with spatial modelling, can support the implementation and assessment of new and more integrated weed management strategies. Our review focuses on the biological and mathematical aspects of assembling within-field weed models. We describe both static and spatio-temporal models of within-field weed distributions (including both cellular automata (CA) and non-CA models), discussing issues surrounding the spatial processes of weed dispersal and competition and the environmental and anthropogenic processes that affect weed spatial and spatio-temporal distributions. We also examine issues surrounding model uncertainty. By reviewing the current state-of-the-art in both static and temporally dynamic weed spatial modelling we highlight some of the strengths and weaknesses of current techniques, together with current and emerging areas of interest for the application of spatial models, including targeted weed treatments, economic analysis, herbicide resistance and integrated weed management, the dispersal of biocontrol agents, and invasive weed species.
Highlights
Weeds are the main source of yield loss in crops, causing up to 34% loss across agricultural and horticultural crop production [1,2]
Spatio-temporal models can predict the local development of weed populations, which may be useful in mapping pre-emergent herbicide use, within-field distribution can be unpredictably variable [6]
The greatest range of development in weed modelling is currently observed for the spatio-temporal models
Summary
Weeds are the main source of yield loss in crops, causing up to 34% loss across agricultural and horticultural crop production [1,2]. The uptake of site-specific weed management is lacking, largely due to the unavailability of accurate within-field weed species distribution maps. Spatio-temporal models can predict the local development of weed populations, which may be useful in mapping pre-emergent herbicide use, within-field distribution can be unpredictably variable [6]. The accurate modelling of static within-field weed distributions can be of benefit to farmers investigating herbicide resistance, checking for invasions of new weed species, and implementing biological control agents. Modelling within-field distributions in this way benefits farmers by supporting site-specific weed management, but such spatial models can provide researchers with an improved understanding of the underlying processes determining weed spatial distributions, and open up new avenues for research and investigation to better understanding of the ecology and biology of agricultural weeds. We identify areas of emerging interest, where models of within-field weed spatial distributions could be instrumental in the development of novel weed management techniques
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
