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Record W2986101099 · doi:10.5539/sar.v8n4p84

Commercialization of Smallholder Pulse Producers in East Gojjam Zone, Ethiopia

2019· article· en· W2986101099 on OpenAlexvenueno aff
Assefa Tilahun, Jema Haji, Lemma Zemedu, Dawit Alemu

Bibliographic record

VenueSustainable Agriculture Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationTobit modelAgricultural economicsLivestockDescriptive statisticsAgricultural scienceBusinessAgricultureEconometric modelEconomicsGeographyMarketingMathematicsStatistics

Abstract

fetched live from OpenAlex

This study examines pulse producers’ commercialization using a cross-sectional data obtained from 385 randomly and proportionately selected sampled households from East Gojjam zone, Amhara National Regional State of Ethiopia. Data were analyzed using descriptive statistics and econometric model to characterize sample households and identify factors affecting pulse output commercialization. The mean commercial index for the sample households was 0.345 which indicates that on average a household sold 34.5% of his/her total pulse produce. As a result, farm households’ output commercialization levels fall in semi-commercial farming system. Two limit Tobit model result indicated that farm households’ crop output commercialization was positively and significantly influenced by access to improved seed, cooperative membership, land size, access to market information and pulse yield and was negatively and significantly influenced by family size and livestock owned. Based on the findings, improved seed/new varieties should be released and accessed to smallholder farmers, deliver market information timely, land owned allocation should be intensified so that smallholder producers can increase their crop output commercialization, strengthening the existing farmers’ cooperatives and finally cut and carry livestock feeding system should be practiced in order to manage farm land properly.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.321
Teacher spread0.267 · 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.

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

Citations14
Published2019
Admission routes1
Has abstractyes

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