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Record W3005234161 · doi:10.5539/sar.v9n2p30

Technical Efficiency of Moringa Production: A case Study in Wolaita and Gamo Zones, Southern Ethiopia

2020· article· en· W3005234161 on OpenAlexvenueno aff
Alula Tafesse, Degye Goshu, Fekadu Gelaw, Alelign Ademe

Bibliographic record

VenueSustainable Agriculture Research · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
FundersHaramaya UniversityMinistry of Education, IndiaMinistry of Education, Ethiopia
KeywordsMoringaProduction (economics)ProductivityLivelihoodAgricultural economicsBusinessAgricultural productivityAgricultureAgricultural scienceEconomicsGeographyEnvironmental scienceEconomic growth

Abstract

fetched live from OpenAlex

Moringa has been becoming among vastly growing and trading commodities in different parts of Ethiopia for its multiple benefits. However, empirical researches analyzing its productivity at smallholder farmer level were missing. This study aimed to fill the existing gap with a cross-sectional survey study on sampled 117 Moringa producer farmers from southern Ethiopia. The Stochastic Frontier Model was used to estimate the level and factors determining the technical efficiency of Moringa production. The collected data fitted Cobb-Douglas production function with inputs, labor and the numbers of trees positively and significantly determined the output of Moringa. An estimated level of efficiency shows farmers have the possibility to increase Moringa output by 47.81% with existing inputs and technology. The land, off-farm activities, access to road, credit, and irrigation were significant factors affecting the technical efficiency of Moringa. It requires policies and development actions to perform on mechanisms to advance the production of Moringa. Hence, any development direction to enhance Moringa production should consider households with limited access to land and irrigation. Furthermore, the development of road infrastructure is required to increase agricultural productivity. In sum, modern credit institutions, as well as facilities, found essential to improve the livelihood of Moringa producers in the area.

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)0.015
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.012
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.126
GPT teacher head0.429
Teacher spread0.303 · 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 designQualitative
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

Citations1
Published2020
Admission routes1
Has abstractyes

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