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Record W4383961252 · doi:10.5539/jas.v15n8p48

Valuing Pollination as an Ecosystem Services: The Case of Hand Pollination for Cocoa Production in Ghana

2023· article· en· W4383961252 on OpenAlexvenueno aff
Salamatu Jebuni-Dotsey, Bernardin Senadza, Wisdom Akpalu

Bibliographic record

VenueJournal of Agricultural Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
Fundersnot available
KeywordsPollinatorPollinationEcosystem servicesAgroforestryPopulationBusinessAgricultural sciencePollination managementAgricultural economicsEconomicsBiologyEcosystemEcology

Abstract

fetched live from OpenAlex

The promotion of cocoa farm productivity has necessitated the intensification of input use with ensuing loss of natural pollinators. Ghana Cocoa Board’s (COCOBOD) remedy to declining pollinator population is addressed in the rolling out of hand pollination in the 2016/17 crop year. Applying contingent valuation on field data covering 608 farmers in five cocoa growing regions, we estimate the value of pollinator services to the cocoa industry in Ghana and farmers willingness to pay for the service. We find that cocoa farmers in Ghana are willing to pay for hand pollination to improve on their farm yields. Farmers averagely value pollinator services at $1.3 per acre of land. Extrapolated to cover all cultivated cocoa lands for 2017/18 crop year, the value of pollinator services to Ghana’s cocoa industry is averagely $6.1 million per annum. Hand pollination can improve cocoa farms yields given the statistically significant mean difference in yields between hand-pollinated and non-hand-pollinated farms. Having established the loss to the cocoa industry from pollinator decline and the need for effective pollination to support crop productivity, it is imperative for COCOBOD to ramp up strategies at preserving cocoa farm ecology to safe guard the industry.

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)0.000
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 score0.871
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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
Published2023
Admission routes1
Has abstractyes

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