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Record W2756095535 · doi:10.5539/jas.v9n10p11

Constraints to Vegetable Production Resulting from Pest and Diseases Induced by Climate Change and Globalization: A Review

2017· review· en· W2756095535 on OpenAlexvenueno aff
Mutondwa M. Phophi, Paramu Mafongoya

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersInyuvesi Yakwazulu-NataliNational Research Foundation
KeywordsBiosecurityAgricultureDistribution (mathematics)PEST analysisClimate changeGreenhouse gasNatural resource economicsAgroforestryGlobal warmingIntegrated pest managementAgricultural productivityProduction (economics)Insect pestGlobalizationGeographyAgronomyBiotechnologyEcologyBusinessBiologyEconomics

Abstract

fetched live from OpenAlex

Vegetable production worldwide is constrained by pests and diseases which effects are exacerbated by climate change and variability. Greenhouse gas emissions are also increasing due to poor agricultural practices and other human activities. This will continue to have a negative impact on the prevalence of insect pests and diseases. This review focuses on the climatic factors that impact on insect pests and diseases of vegetable crops. High atmospheric temperatures and elevated carbon dioxide increases pest development, survival of pests and distribution of pest to new areas. The distribution of insect pests and diseases are not due to climate changes only but are also a result of globalisation and poor biosecurity measures at country borders. There is limited information on the distribution of pests and diseases due to globalisation in African countries. New exotic pests will continue to be introduced to countries if biosecurity measures are not improved. Future research must focus on how to manage emerging pests and diseases influenced by high temperatures and carbon dioxide and other climatic conditions which influence pest severity under smallholder farmers in the southern African regions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.992
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.092
GPT teacher head0.316
Teacher spread0.223 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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