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Record W2094620232 · doi:10.5558/tfc81345-3

Production constraints on cocoa agroforestry systems in West and Central Africa: The need for integrated pest management and multi-institutional approaches

2005· article· en· W2094620232 on OpenAlexvenueno aff
Dénis Sonwa, Stéphan Weise, A. Adesina, A.B. Nkongmeneck, M. Tchatat, O. Ndoye

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
Fundersnot available
KeywordsAgroforestryAgricultureSustainable managementGeographyIntegrated pest managementProduction (economics)Agricultural economicsBusinessSustainabilityEconomicsEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Cocoa-producing countries of West and Central Africa experienced a serious economic crisis in the early 1980s,when the cocoa sector was liberalized and the macroeconomic policies of the sector changed. These institutional changes created new difficulties and challenges for sustainable cocoa farming. Farmers in this region have recently turned to timber and non-timber production to offset the fluctuation of cocoa prices. In a survey of 300 cocoa farmers in the humid forest zone of Southern Cameroon, pest and disease outbreaks were identified as the major limiting factors to sustainable cocoa production. An analysis of pests and diseases affecting the cocoa plantations in the humid forest zone of West and Central Africa revealed strong links to the type of forest cover found on or near the cocoa plantation. An integrated approach to pest management is proposed and the paper concludes with a discussion of current efforts to address constraints posed by pests and diseases on sustainable cocoa farming in the four main cocoa-producing countries of West and Central Africa. Key words: cocoa agroforest, farmer perception, forest landscape, multi-disciplinary approach, multi- institutional approach, Africa

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.042
GPT teacher head0.216
Teacher spread0.174 · 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 designObservational
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

Citations27
Published2005
Admission routes1
Has abstractyes

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