通过机器学习揭示了新西兰未来抗除草剂杂草的潜在风险

IF 1.5 3区 农林科学 Q2 AGRICULTURE, MULTIDISCIPLINARY
P. Hulme
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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.
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来源期刊
CiteScore
4.90
自引率
16.70%
发文量
31
审稿时长
3 months
期刊介绍: The New Zealand Journal of Agricultural Research publishes original research papers, review papers, short communications, book reviews, letters, and forum articles. We welcome submissions on all aspects of animal and pastoral science relevant to temperate and subtropical regions. The journal''s subject matter includes soil science, fertilisers, insect pests, plant pathology, weeds, forage crops, management systems, agricultural economics, agronomy, and animal science. The journal also accepts crossover papers on subjects such as land –water interactions.
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