MétaCan
Menu
Back to cohort
Record W2969534565 · doi:10.1111/1746-692x.12230

Agricultural Risk Management in the European Union: A Proposal to Facilitate Precautionary Savings

2019· article· en· W2969534565 on OpenAlexaff
Marcel van Asseldonk, R.A. Jongeneel, G. Cornelis van Kooten, Jean Cordier

Bibliographic record

VenueEuroChoices · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRevenueCrop insuranceRisk managementEuropean unionBusinessProduction (economics)PaymentAgricultureSafety netAgricultural economicsEconomicsEconomic policyFinancePolitical science

Abstract

fetched live from OpenAlex

Summary Through a series of reforms, the European Union ( EU ) replaced most of its trade distorting price support programmes with safety net provisions and direct payments decoupled from production. This has resulted in greater market orientation and a situation in which farmers face increased price variability. Policy now emphasises the development of business risk management ( BRM ) programmes, such as crop and whole farm insurance. However, for various reasons EU ‐wide adoption of BRM programmes and farmer uptake and use of risk instruments is below expectations. We recommend the use of farm‐specific savings accounts upon which farmers can draw when revenues fall below a proportion of expected revenue. Farmer‐Directed Precautionary Savings Accounts ( FDPSA s) would complement traditional non‐financial, on‐farm risk management strategies and private/public risk transfer strategies. FDPSA s would protect farmers against shallow losses and, along with crop and/or index‐based insurance, also protect against deep losses. Further, this form of protection is easy to understand and administer and leaves complete control with the producer.

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)0.001
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 score0.698
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.019
GPT teacher head0.204
Teacher spread0.185 · 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

Explore more

Same venueEuroChoicesSame topicAgricultural Economics and PolicyFrench-language works237,207