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Record W4403659939 · doi:10.1111/agec.12859

An experimental approach to farmer valuation of African rice genetic resources

2024· article· en· W4403659939 on OpenAlexaff
Nicholas Tyack, Aminou Arouna, Rachidi Aboudou, Marie-Noëlle Ndjiondjop

Bibliographic record

VenueAgricultural Economics · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsValuation (finance)Genetic resourcesEconomicsNatural resource economicsAgricultural economicsBusinessBiotechnologyBiologyFinance

Abstract

fetched live from OpenAlex

Abstract Genebanks serve as both providers of valuable traits for breeding programs and repositories of diverse crop genetic material representing society's agricultural heritage. In this study, we use a Becker‐DeGroot‐Marschak mechanism to elicit the willingness‐to‐pay of rice farmers in Côte d'Ivoire for small amounts of African rice ( Oryza glaberrima ) landraces held by the genebank of the Rice Biodiversity Center for Africa, and for seed of newly developed ARICA rice varieties bred using genebank materials. Using a field experiment, we additionally investigate how randomized exposure to and experimentation with small amounts of African rice landrace seed or seed of advanced rice varieties developed by AfricaRice affect how smallholder rice farmers value these novel genetic resources. Surprisingly, we find that farmers generally value having access to African rice landraces at approximately the same level as for advanced rice varieties (and far above market rates for improved seed), and that those farmers who grew landrace seed in the offseason were willing to pay more than those who did not. Our results demonstrate the additional value provided by the conservation of African rice landrace varieties (apart from their use in breeding) and highlight the importance of experimentation in the adoption process.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.254
Teacher spread0.207 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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