Spatial Modelling of Within-Field Weed Populations; a Review

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.

Monitoring the Spatial Variability of Knapweed (Centaurea diluta Aiton) in Wheat Crops Using Geostatistics and UAV Imagery: Probability Maps for Risk Assessment in Site-Specific Control

“…The cokriged maps exhibited more local detail and less smoothness in their depiction of the variability of weed density. Comparable results were found for other weed species cokriged with UAV-derived variables as covariates [5,13,23]. The improved local detail of the cokriging maps was due to the finer spatial resolution of the UAV-derived secondary variables.…”

“…For numerous weed species, it was found that patches occurred more or less at the same location over years indicating that the within-field weed distributions are relatively stable between years. If weeds are expected at the same locations over years, this makes any map produced useful in future years ( [6,13,88,89], among many others). Although, the greater the time between sampling and prediction, the less accurate the prediction becomes [13].…”

“…Site-specific management is already commonplace in many aspects of farming. However, the uptake of SSWM is lacking, largely because of the unavailability of accurate within-field weed species distribution maps [13].…”

“…To develop effective SSWM strategies based on maps, robust methods for weed data acquisition and analysis procedures that integrate knowledge about the within-field spatial variability of infestations are required [5,13]. Regrettably, traditional weed sampling is not viable, as such information cannot be obtained exclusively from traditional weed sampling at a number of discrete locations because it is costly, labor-intensive, and requires a large number of weed field samples to achieve a good representation of weed infestations.…”

Applications of Biotechnology for Characterization of Plants and Pests as the Key Components of Plant Protection and Production Strategies: A Review

“…Weather monitoring systems are developed to more accurately predict pest occurrence and spread as a result of increasing temperature, frontal patterns, and other atmospheric conditions as well as anthropogenic factors (CAST, 2017). In summary, several quantitative models to determine the spread of plant pests for use in pest risk assessment has been implemented such as for pathogens (Dalal and Singh, 2017), insects (Tonnang et al, 2017;Balikai et al, 2019) and weeds (Hanzlik and Gerowitt, 2016;Somerville et al, 2020).…”