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Record W2801979946 · doi:10.5539/sar.v7n3p1

Positive Yield Impact of Predacious Mites in Tea Production Areas of Kenya

2018· article· en· W2801979946 on OpenAlexvenueno aff
Daniel L. Mutisya, E. M. El Banhawy, Evelyn Cheramgoi

Bibliographic record

VenueSustainable Agriculture Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsPhytoseiidaeBiologyMiteSpider miteAltitude (triangle)PEST analysisFertilizerAgronomyCanopySpiderCropHorticulturePredatorBotanyPredationEcology

Abstract

fetched live from OpenAlex

Tea agro ecosystems are least disturbed in comparison to other crop systems due to the plant canopy type. A survey on predacious mites’ abundance and impact on leaf yield in different tea production areas was carried out in 2014-2015 in Kenya. Major pest species were the red spider mite Oligonychus coffeae and the yellow thrip, Scirtothrips dorsalis. The red crevice mite, Brevipalpus phoenicis was found in few areas and in low numbers. Predacious mites of Phytoseiidae Family were highest at 5.8 and 6.8 mites per sample during dry and wet seasons, respectively. Phytoseiid abundance during the dry and wet seasons did not differ much within most sample sites. Presence of phytoseiid mites in combination with NPK-fertilizer-high altitude led to the strongest correlation to leaf yield than any other combination variable. High phytoseiid density- high altitude was third with positive impact to yield increase after high altitude combined with NPK-fertilizer. The findings here show contributory positive impact of phytoseiids in tea leaf yield in an agro eco-system.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.300
Teacher spread0.281 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2018
Admission routes1
Has abstractyes

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