Potential risks of future herbicide-resistant weeds in New Zealand revealed through machine learning

ABSTRACT

New Zealand has fewer numbers of herbicide-resistant crop weeds than many other highly developed economies, yet these numbers are likely to increase in the future. A clear indication of the scale of this risk can be derived from the predictable structure in the global occurrence of herbicide-resistant weeds that results from similarities in agronomic and environmental conditions. Hierarchical cluster analysis was used to identify groups of countries with similar herbicide-resistant weed assemblages to New Zealand. A distinct cluster of 11 European countries with strong climatic and agronomic affinities to New Zealand was identified. The combined assemblage of herbicide-resistant weeds within this cluster consisted of 27 species and the potential risk of a species evolving herbicide resistance was calculated as its frequency among these European countries. Species with potential to become herbicide resistant in New Zealand included established crop weeds (e.g. Senecio vulgaris, Tripleurospermum inodorum) as well as species only encountered as contaminants of seed imports (e.g. Alopecurus myosuroides, Apera spica-venti). All eight species already known to be herbicide-resistant in New Zealand were found in the high-risk assemblage and this indicates that the analysis provided a realistic measure of future risk.

KEYWORDS:

• Arable crops
• machine learning
• risk assessment
• sustainable agriculture
• weed management
• weeds

Acknowledgements

The author thanks Ian Heap for access to a subset of the International Herbicide-Resistant Weed Database. The research was funded by the New Zealand Ministry of Business, Innovation and Employment under the project ‘Managing Herbicide Resistance’ (grant number C10X1806).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data used in this research are available from the International Herbicide-Resistant Weed Database (www.weedscience.org).

Additional information

Funding

This work was supported by Ministry for Business Innovation and Employment [grant number C10X1806].