Alternative plant protection strategies for tomorrow's coffee

Athina Koutouleas*, David B. Collinge, Anders Ræbild

*Corresponding author for this work

Research output: Contribution to journalReviewResearchpeer-review

15 Citations (Scopus)
28 Downloads (Pure)

Abstract

Continuous pesticide usage has negative impacts on people and ecosystems associated with coffee farms. Alternative plant protection strategies can be implemented that are sustainable for both the environment and the coffee farmer. In this review, new genomic techniques (NGTs) such as RNAi (RNA interference, using spray-induced gene silencing – SIGS) are presented as a possible novel strategy to manage Coffea arabica pests and diseases. Exploitation of the coffee agroforestry system (AFS) is presented as another strategy, offering both plant protection and ecosystem restoration functions. Interactions within a coffee-AFS were found to both hinder and bolster the development of some coffee pests and diseases. Biological control represents a third strategy that has been examined to-date to combat important coffee pests and diseases (i.e., American leaf spot, black coffee twig borer, coffee berry borer, coffee berry disease, coffee leaf miner, coffee leaf rust, coffee wilt disease and green coffee scale). The astute use of RNAi, AFS and/or biological control have the potential to provide alternatives to conventional pesticides for future sustainable coffee production. However, these approaches must be compatible with the coffee farmers' local needs and accessibility and bolstered through nationwide support by advisory services and coffee authorities.

Original languageEnglish
JournalPlant Pathology
Volume72
Issue number3
Pages (from-to)409-429
ISSN0032-0862
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Plant Pathology published by John Wiley & Sons Ltd on behalf of British Society for Plant Pathology.

Keywords

  • agroforestry
  • biological control
  • Coffea arabica
  • pesticides
  • RNAi
  • smallholder farmer

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