MétaCan
Menu
Back to cohort
Record W4308999696 · doi:10.5539/jas.v14n12p101

Involvement of Women in Adopting Climate Change Adaptation Practices in Cacao Farming in Côte d’Ivoire

2022· article· en· W4308999696 on OpenAlexfundvenueno aff
Ouattara Yerayou Céline, M’bo Kacou Antoine Alban, Mamadou Chérif, Souleymane Sanogo, Renée Brunelle, Leblanc Caroline, Daouda Koné

Bibliographic record

VenueJournal of Agricultural Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
FundersWest African Science Service Centre on Climate Change and Adapted Land UseInternational Development Research Centre
KeywordsAgricultureGeographyClimate changeSocioeconomicsSowingAgroforestryAgricultural scienceBiologyAgronomyEcologyEconomics

Abstract

fetched live from OpenAlex

The adverse effects of climate change are leading producers to adopt endogenous strategies. Nevertheless, the involvement of women in the adoption of adaptation practices was assessed in the localities of Abengourou, Gagnoa, Soubré and Vavoua. Interviews with cocoa farmers, 69 female and 288 male, show that drought (77.8%) is one of the most observed climatic factors by farmers in cocoa farms. In order to reduce the effects, 27% of women farmers preferred to set up nurseries close to water points and at home, compared to 15.2% of men. 73.7% Women leave the plants for 4 months before planting compared to 59.2% of men. In soil fertility management, 67.9% of women use leguminous plants as cover crops, compared to 52.8% of men. During the rainy season, they ferment the beans for 6 days (35% compared to 22% of men) and harvest the pods at least once a week (16% compared to 1% of men) in the absence of rain. Women are strongly involved in the adoption of practices at all stages of cocoa production. In terms of adaptation, it would be important that the practices identified be integrated into the training of women producers to enable them to be resilient.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.060
GPT teacher head0.264
Teacher spread0.204 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Agricultural ScienceSame topicCocoa and Sweet Potato AgronomyFrench-language works237,207