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Record W619837858

Marginal Value of Irrigation Water Use in the South Saskatchewan River Basin, Canada

2009· article· en· W619837858 on OpenAlexaboutno aff
Antony Samarawickrema, Suren Kulshreshtha

Bibliographic record

VenueLincoln (University of Nebraska) · 2009
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMarginal valueIrrigationWater useCash cropAgricultureMarginal productMarginal costEnvironmental scienceAgricultural economicsWater resource managementFarm waterCommodityClimate changeValue (mathematics)Marginal landWater conservationEconomicsGeographyProduction (economics)AgronomyMathematicsEcology
DOInot available

Abstract

fetched live from OpenAlex

The allocation of water is part of water management. In order to achieve maximum benefits to society, water should be allocated toward uses that have the highest value, followed, as an alternative, by the next highest level or one with equal value. Such decisions require knowledge of water value at the last unit of use. Within agriculture, irrigation is important. Irrigation water must be allocated to various crops; therefore, producers require knowledge of the marginal value of water among alternative crops. This study estimates marginal value product for irrigation water within the southern areas of the Canadian Prairie Provinces using a crop-response model. Marginal values were estimated under the present and a future climate scenario. Cash crops such as potatoes and dry beans had higher marginal values of water, around $1,000 per 1,000 m3. Cereals and oilseed crops lagged behind (close to $200 per 1,000 m3). Results show modest increases in marginal value under climate change, compared to the volatility resulting from commodity market price changes seen today.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.941

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.138
Teacher spread0.132 · 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.

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

Citations6
Published2009
Admission routes1
Has abstractyes

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