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Cited by 245

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Insect Pest Management in Tropical Asian Irrigated Rice

Matteson, P. C.·2000·Annual Review of Entomology

Abstract

▪ Abstract Abundant natural enemies in tropical Asian irrigated rice usually prevent significant insect pest problems. Integrated pest management (IPM) extension education of depth and quality is required to discourage unnecessary insecticide use that upsets this natural balance, and to empower farmers as expert managers of a healthy paddy ecosystem. Farmers' skill and collaboration will be particularly important for sustainable exploitation of the potential of new, higher-yielding and pestresistant rices. IPM “technology transfer” through training and visit (T&V) extension systems failed, although mass media campaigns encouraging farmer participatory research can reduce insecticide use. The “farmer first” approach of participatory nonformal education in farmer field schools, followed by community IPM activities emphasizing farmer-training-farmer and research by farmers, has had greater success in achieving IPM implementation. Extension challenges are a key topic for rice IPM research, and new pest management technology must promote, rather than endanger, ecological balance in rice paddies.

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Metadata

Authors
Matteson, P. C.
Venue
Annual Review of Entomology
Published
2000-01
Volume
45
Issue
1
Pages
549-574
ISSN
  • 0066-4170 (ISSN-L)
  • 1545-4487
OpenAlex
W2163391762
Cited by
245
References
4
FWCI
1.48
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Insect Pest Management in Tropical Asian Irrigated Rice. https://app.cochat.ai/papers/10.1146/annurev.ento.45.1.549
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Matteson, P. C. (2000). Insect Pest Management in Tropical Asian Irrigated Rice. Annual Review of Entomology. https://doi.org/10.1146/annurev.ento.45.1.549

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.