Evidence map›Paper›PMID 42213498›Full record

ArticleEnvironmental entomology2026

Pest risk modelling for climate-smart vegetable production in East Africa.

Marc Marin Agbodjan, Komi Mensah Agboka, John Odindi, Elfatih Abdel-Rahman, Onisimo Mutanga, Frank Chidawanyika, Chrysantus Tanga, Daniel Munyao Mutyambai, Sevgan Subramanian, Bonoukpoè Mawuko Sokame

Abstract read
In one paragraph

Article in Environmental entomology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Marc Marin AgbodjanInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.
Komi Mensah AgbokaInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.
John OdindiSchool of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
Elfatih Abdel-RahmanInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.
Onisimo MutangaSchool of Agriculture and Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
Frank ChidawanyikaInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.
Chrysantus TangaInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.ORCID 0000-0002-5788-7920
Daniel Munyao MutyambaiInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.ORCID 0000-0003-0556-1707
Sevgan SubramanianInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.ORCID 0000-0003-4447-0744
Bonoukpoè Mawuko SokameInternational Centre of Insect Physiology and Ecology (icipe), Nairobi 30772-00100, Kenya.ORCID 0000-0003-3990-6697

Funding

German Federal Ministry for Economic Cooperation and Development (BMZ)Government of NorwayGovernment of the Republic of KenyaIKEA Foundation G-2204-02144Swedish International Development Cooperation AgencySwiss Agency for Development and Cooperation; the Australian Centre for International Agricultural Research
6 · The paper itself

Abstract

Agricultural productivity in East Africa is increasingly threatened by climate-sensitive insect pests. This study investigates the occurrence of key vegetable pests: Brevicoryne brassicae, Plutella xylostella, Maruca vitrata, and Liriomyza mik. A comparative species distribution modelling evaluated pest risk projections under climate change. Four variable selection strategies were tested: case 1 (correlation and variance inflation factor [VIF] filtering across all predictors), case 2 (correlation and VIF filtering of bioclimatic variables merged with other predictors), case 3 (filtering based on environmental values at species occurrence point to approximate realized niches), and case 4 (dimensionality reduction using principal component analysis). These strategies were combined with 3 algorithms: generalized linear model, boosted regression tree, and maximum entropy. In total, 640 spatially occurrence records were analyzed with bioclimatic, elevation, normalized difference vegetation index, solar radiation, wind speed, and land use predictors. Future projections were generated under shared socioeconomic pathways, SSP2-4.5 and SSP5-8.5 (2041-2060), using 4 general circulation models. Model performance varied by species and strategy: case 3 performed best for B. brassicae and P. xylostella, case 1 was most effective for L. mik, and case 2 performed best for M. vitrata. Current suitability maps highlighted high risk zones in central and western Kenya, southwestern Uganda, and Rwanda's northern province. Projections indicated consistent expansions for P. xylostella and L. mik, while B. brassicae and M. vitrata exhibited localized and uncertain shifts. These findings provide decision-ready pest risk maps that can inform climate-smart pest management, including targeted surveillance, resistant crop varieties, and biological control strategies across in East African vegetable systems.

Indexed as

AgricultureEcosystemInsectaAfrica, EasternAnimalsClimate ChangeKenyaPest ControlPredictive Learning ModelsRisk AssessmentRwandaUgandaclimate changeensemble modelingspecies distribution modelingvegetable pest risk mapping

Identifiers

PMID42213498
PMCPMC13220763

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.