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Record W2200138134 · doi:10.1111/cjag.12093

Effects of Farm Size on Technical Efficiency in China's Broiler Sector: A Stochastic Meta‐Frontier Approach

2015· article· en· W2200138134 on OpenAlexvenueno aff
Xiangfei Xin, Yi Zhang, Jimin Wang, J. Alexander Nuetah

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
FundersAgricultural Science and Technology Innovation ProgramNational Development and Reform Commission
KeywordsBroilerChinaMathematicsStochastic frontier analysisForestryAnimal scienceAgricultural scienceHumanitiesGeographyBiologyEconomicsProduction (economics)ArtMicroeconomics

Abstract

fetched live from OpenAlex

This paper employs the stochastic meta‐frontier approach to measure technical efficiency and to investigate the effects of farm size on the technical efficiency in China's broiler sector. Empirical results show a positive association between farm size and technical efficiency in China's broiler sector. The medium and large farm sizes exhibit increases of 0.058 and 0.160, respectively, in technical efficiency scores, relative to small farms, which have a mean technical efficiency score of 0.722. Results indicate that there are significant differences in technical efficiency across regions. Technical efficiency in the southern region, which is dominated by yellow‐feathered broilers, is significantly lower than that in the northern region where white‐feathered broilers are the dominant species. Also, the technical efficiency scores estimated from the meta‐frontier model vary substantially across farm sizes in the southern region. Increased farm size improves the technical efficiency for yellow‐feathered broiler production. Dans la présente étude, nous avons utilisé l'analyse métafrontière stochastique pour mesurer l'efficience technique et pour déterminer les effets de la taille de la ferme sur l'efficience technique du secteur du poulet à griller en Chine. Les résultats empiriques indiquent qu'il existe un lien positif entre la taille de la ferme et l'efficience technique. Les fermes de moyenne et grande taille affichent une augmentation du pointage d'efficience technique de 0,058 et de 0,160 respectivement, comparativement aux fermes de petite taille dont le pointage d'efficience technique moyen est de 0,722. Les résultats indiquent que l'efficience technique varie considérablement d'une région à l'autre. L'efficience technique dans la région du Sud, où l’élevage du poulet à griller blanc domine, est significativement plus faible que celle de la région du Nord, où l’élevage du poulet à griller jaune domine. De plus, les pointages d'efficience technique estimés à partir du modèle de métafrontière varient considérablement dans la région du Sud. L'augmentation de la taille de la ferme améliore l'efficience technique de la production du poulet à griller jaune.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.054
GPT teacher head0.232
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations23
Published2015
Admission routes1
Has abstractyes

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