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Record W2802175507 · doi:10.7939/r3xp6vg20

Expanding Irrigated Agriculture in Alberta: An Economic Impact Assessment

2017· article· en· W2802175507 on OpenAlexaboutno aff
Dareskedar Workie Amsalu

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigated agricultureAgricultureEconomic impact analysisIrrigationWater resource managementAgricultural economicsEnvironmental planningNatural resource economicsGeographyEnvironmental scienceEconomicsAgronomy

Abstract

fetched live from OpenAlex

This study assessed the economic impacts of Alberta’s irrigated agriculture industry as of 2011 and evaluated the economic viability of expanding the irrigated crop land by 10% within the 13 irrigation districts in southern Alberta. Results of the economic impact assessment revealed that irrigation, directly or indirectly, generated $3.2 billion to the national gross domestic product. The distribution of these benefits was 17% for producers and 83% for the province and the nation. Results of the economic viability analysis revealed that with the existing government subsidy of 75% to the irrigation rehabilitation program, investment for expansion of irrigated crop land would be economically viable for producers. However, in the absence of this effective government subsidy, the investment would be unattractive. The results are consistent with the fact that irrigation expansion is a capital-intensive project and as such its economic viability for producers is contingent upon the levels of subsidy and the opportunity costs of capital. The results have important policy implications for the provision of economic incentives for producers investing in water saving irrigation technologies.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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