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Record W2769403974 · doi:10.5539/jas.v9n12p160

Responses of Insect Pests and Plant Diseases to Changing and Variable Climate: A Review

2017· review· en· W2769403974 on OpenAlexvenueno aff
Rumbidzai Debra Katsaruware-Chapoto, Paramu Mafongoya, Augustine Gubba

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersNational Research Foundation
KeywordsTemperate climateInsectClimate changeBiologyEcologyPrecipitationTropicsAbundance (ecology)Global warmingPEST analysisExtreme weatherInsect pestAdaptation (eye)Environmental scienceAgronomyGeographyBotany

Abstract

fetched live from OpenAlex

Natural and anthropogenic factors have resulted in altered environmental conditions that influence changes in abundance and diversity of insect pests. Global climate change projections focus on crop yields and adaptation strategies to declining yields and ignore the likely impact of a changing climate on insect pests and plant diseases. In this research paper, we review the effects of climate variables namely temperature, carbon dioxide (CO2), precipitation and extreme weather events on insect pests and plant diseases incidence. Elevated temperatures, CO2 and extreme weather events have been shown to alter the distribution, reproductive potential, the incidence and abundance of plant insects and diseases in temperate regions because of the dependence of insects and diseases on environmental conditions. There is limited information on the influence of temperature and carbon dioxide as well as their interaction on the incidence and severity of insect pests, bacterial and viral diseases in the tropical regions. Information on the influence of altered precipitation patterns is also limited but could be of importance in insect distribution studies in a changing climate. Some tropical insects pests are most likely to suffer from extreme heat, resulting in death and hence pest extinction. Future research should focus on the interaction of elevated temperature and CO2, determine the influence of supra optimal summer temperatures, temperature variability, precipitation variability and the corresponding viral and bacterial diseases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.306
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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