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

被引:1
|
作者
Hulme, Philip E. E. [1 ]
机构
[1] Lincoln Univ, Dept Pest Management & Conservat, Christchurch, New Zealand
关键词
Arable crops; machine learning; risk assessment; sustainable agriculture; weed management; weeds; SELF-ORGANIZING MAPS; K-MEANS; PESTS;
D O I
10.1080/00288233.2023.2210288
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
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.
引用
收藏
页码:17 / 27
页数:11
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