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Record W2794119346 · doi:10.5897/jdae2017.0841

Use of a warrantage system to face rural poverty and hunger in the semi-arid area of Burkina Faso

2018· article· en· W2794119346 on OpenAlexafffund
Ouattara Badiori, Jean Baptiste Taonda Sibiri, Traore Arahama, Idriss Sermé, François Lompo, Peak Derek, P. Sedogo Michel, Bationo Andre

Bibliographic record

VenueJournal of Development and Agricultural Economics · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversity of Saskatchewan
FundersInternational Development Research CentreAlliance for a Green Revolution in AfricaUniversity of Saskatchewan
KeywordsLivelihoodPovertyBusinessAgricultureAgricultural economicsAgricultural productivityProductivityEconomic growthSocioeconomicsEconomicsGeography

Abstract

fetched live from OpenAlex

It is widely believed that limited access of small scale farmers to agricultural credit is one of the key causes of rural poverty and a major constraint to adoption of innovations in Sub-Saharan Africa. Since the early 1960s, many strategies to access agricultural credits have been implemented with success. This study assessed the effects of warrantage, a community-based micro credit system, on poor small resource farmers’ income and livelihoods of the semi-arid area of Burkina Faso. Two broad socio economic surveys were conducted among 1040 farmers and 440 household heads. Data were collected from 58 inventory credit warehouses and 36 input shops established in the study areas. The results showed that the warrantage system is dominated by women farmers (who produce 60% of the stored harvests) and appears as the main source of agricultural credit.  The profit (up to 140%) provided allows farmers to purchase external inputs such as inorganic fertilizers. This resulted in higher crop productivity and a substantial increase of farmers’ income which in turn improve farmers’ livelihood.   Key words: Inventory credit system, mineral fertilizer, staple crop production, small farmers.

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 score0.648
Threshold uncertainty score0.194

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 head0.022
GPT teacher head0.209
Teacher spread0.186 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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