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Record W2544103630 · doi:10.1111/cjag.12122

An Experimental Approach to the Investment Timing of Conventional and Organic Hog Farmers

2016· article· en· W2544103630 on OpenAlexvenueno aff
Elisabeth Vollmer, Daniel Hermann, Oliver Mußhoff

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsOrganic farmingInvestment (military)Agricultural scienceProduction (economics)European unionOrganic productionEconomicsAgricultureWelfare economicsBusinessAgricultural economicsMicroeconomicsPolitical scienceEnvironmental scienceGeographyEconomic policy

Abstract

fetched live from OpenAlex

Politicians throughout the European Union support the extension of organic farming. Nevertheless, farmers are reluctant to invest in organic farming. Because nonmonetary aspects might be the reasons for this reluctance, this paper analyzes whether the farmers’ investment behavior varies when given the option to invest in organic and conventional production methods. For this purpose, an incentivized experiment was conducted wherein conventional and organic hog farmers had the opportunity to consecutively invest in an organic and a conventional hog barn. Then, it was determined whether the farmers’ investment timings differed between investments in the currently applied production method and the currently not‐applied production method. These investment situations only differed in their description; the economic conditions were identical. The farmers’ investment times in both production methods were compared to theoretically optimal investment times according to the real options approach (ROA). The results of the analysis show that a significant framing effect was associated with farmers’ response to investment opportunities in organic and conventional hog barns. Farmers invested later if they invested in the currently not‐applied production method. In these cases, the deviations between the ROA and the farmers’ investment time were lower.Les politiciens de l'Union européenne appuient l'expansion de l'agriculture biologique. Pourtant, les éleveurs hésitent à s'y investir. Puisque des facteurs non économiques pourraient expliquer cette réticence, cet article analyse les variations des décisions d'investissement des producteurs à qui l'on offre un investissement dans les méthodes de production biologiques et classiques. À cette fin, une expérience incitative a été menée où les éleveurs de porcs se voyaient offrir l'occasion d'investir en succession dans une porcherie biologique et une classique. Ensuite, l'on a évalué si le calendrier d'investissements des producteurs différait en fonction de la méthode actuelle de production et celle qui n'est pas actuellement en usage. Ces situations d'investissement ne différaient que par leur description, les modalités économiques étant identiques. Les calendriers d'investissements des éleveurs utilisant les deux méthodes de production ont été comparés en fonction de l'approche des options réelles. Les résultats de l'analyse montrent qu'un effet significatif de formulation s'associe aux réactions des éleveurs vis‐à‐vis des occasions d'investissements dans des porcheries biologiques et classiques. Les producteurs de porcs ont investi plus tard s'ils investissaient dans la méthode de production dont il ne se servait actuellement pas. Dans ces cas, l′écart entre l'option réelle et le calendrier de l′éleveur s'avérait moins grand.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.999
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.172
Teacher spread0.103 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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