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Recent advances in allelopathy for weed control: from knowledge to applications
Abstract
Allelopathy is the biological phenomenon of chemical interactions between living organisms in the ecosystem, and must be taken into account in addressing pest and weed problems in future sustainable agriculture. Allelopathy is a multidisciplinary science, but in some cases, aspects of its chemistry are overlooked, despite the need for a deep knowledge of the chemical structural characteristics of allelochemicals to facilitate the design of new herbicides. This review is focused on the most important advances in allelopathy, paying particular attention to the design and development of phenolic compounds, terpenoids and alkaloids as herbicides. The isolation of allelochemicals is mainly addressed, but other aspects such as the analysis and activities of derivatives or analogs are also covered. Furthermore, the use of allelopathy in the fight against parasitic plants is included. The past 12 years have been a prolific period for publications on allelopathy. This critical review discusses future research areas in this field and the state of the art is analyzed from the chemist's perspective. © 2019 Society of Chemical Industry
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Metadata
- Authors
- Macías, F. A., Mejías, F. J., & Molinillo, J. M.
- Venue
- Pest Management Science
- Publisher
- Wiley
- Published
- 2019-03-06
- Volume
- 75
- Issue
- 9
- Pages
- 2413-2436
- ISSN
- 1526-498X (ISSN-L)
- 1526-4998
- OpenAlex
- W2913976776
- Cited by
- 293
- References
- 122
- FWCI
- 21.00
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.