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Record W2077151867 · doi:10.4296/cwrj3602853

Crop Yield and Water Requirement Relationships for Major Irrigated Crops in Southern Alberta

2011· article· en· W2077151867 on OpenAlexaffvenueabout
D. Rodney Bennett, Ted E. Harms

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsAgriculture Food and Rural Development
Fundersnot available
KeywordsEvapotranspirationIrrigationEnvironmental scienceYield (engineering)Deficit irrigationCropCrop yieldAgronomyWater useWater resourcesWater-use efficiencyIrrigation managementWater supplyWater resource managementAgricultural engineeringEnvironmental engineeringEcologyEngineering

Abstract

fetched live from OpenAlex

Water supplies available for irrigation in southern Alberta are limited. A study was conducted in southern Alberta to develop crop yield and evapotranspiration (ETc) relationships for major irrigated crops based on current maximum potential crop yield data and improved methods for determination of crop evapotranspiration. Production functions for crop yield and the field water supply, which includes irrigation at 80% efficiency, effective precipitation, and stored soil moisture depletion, were subsequently determined. These empirical relationships may be used by water managers, economists, and producers to examine the economic implications of crop yield reductions from water stress due to limited water supplies, less than optimum (deficit) irrigation management, or variation in evaporative demand from year to year in different agro-climatic areas of southern Alberta. Potential yield estimates for different scenarios may be used with relevant economic information to determine optimum water use for irrigation.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.203
Teacher spread0.147 · 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

Citations56
Published2011
Admission routes3
Has abstractyes

Explore more

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