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Record W2346428090 · doi:10.5539/sar.v5n3p1

Knowledge Diffusion and the Adoption of Fertilizer Microdosing in Northwest Benin

2016· article· en· W2346428090 on OpenAlexafffundvenue
David Natcher, Erika Bachmann, Jeremy Pittman, Suren Kulshreshtha, Mohamed Nasser Baco, P. B. Irénikatché Akponikpè, Derek Peak

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsUniversity of Saskatchewan
FundersInternational Development Research Centre
KeywordsFood securityAgricultureProductivityBusinessPsychological interventionAgricultural scienceFood insecurityAgricultural economicsEnvironmental resource managementSocioeconomicsGeographyEnvironmental scienceEconomic growthEconomicsPsychology

Abstract

fetched live from OpenAlex

Soil degradation and low crop productivity negatively affect the food security of smallholder farmers in West Africa. Various agricultural techniques have been developed as components of food security interventions, but their effectiveness in addressing food insecurity in part depends upon farmers’ abilities to adopt these techniques. In this paper we present the results of a social network analysis that tracked the flow of information on fertilizer microdosing from our Project Research team (PRs) to Demonstration Farmers (DFs), and from DFs to other Village Farmers (VF) in the village of Koumagou B in northwest Benin. Our findings indicate that both adoption and project awareness of microdosing were low following two years of field trails. Overall, the DFs failed to spread information or promote learning over the trial period, with only 3 of 20 DFs diffusing knowledge to a significant degree (i.e., out-degree >5). After 2 years of trials, the efforts of PRs and DFs were insufficient to mobilize the network to adopt the microdosing technique.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.853
Threshold uncertainty score0.439

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 communication0.0000.000
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 head0.037
GPT teacher head0.310
Teacher spread0.273 · 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

Citations10
Published2016
Admission routes3
Has abstractyes

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