MétaCan
Menu
Back to cohort
Record W2068681441 · doi:10.3126/aej.v11i0.3654

Integrated Economic and Environmental Assessment of Nitrogenous Fertilizer Application in Canadian Prairies

2010· article· en· W2068681441 on OpenAlexafffundabout
Suren Kulshreshtha

Bibliographic record

VenueJournal of Agriculture and Environment · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsNutrasource
FundersSocial Sciences and Humanities Research Council of CanadaBIOCAP Canada
KeywordsGreenhouse gasAgricultureFertilizerAgricultural economicsBusinessEnvironmental scienceNatural resource economicsGreenhouseAgricultural scienceEconomicsGeographyAgronomyEcology

Abstract

fetched live from OpenAlex

Adoption of mitigation measures to reduce greenhouse gas emissions may affect other members of the society, producing a situation of trade-offs. In this study, such a trade-off is has been analyzed using three aspects of the Canadian society: producers (farm level adopter), environment (through reduction in the GHG emissions),; and regional economy (including rest of the society through lost / gained economic activities). The nutrient management strategy involving the switching nitrogen fertilizer application from a combination of fall and spring application to a 100 percent spring application. Results suggest that the adoption of such a measure creates a 'win-win' situation, being both environmentally and economically desirable. Under the scenario, fertilizer expenditures decreased by $43 million (giving rise to an equivalent increase in farm income), GHG emissions (in CO2E) by 2.15 percent of the 2000 level of emissions, Canadian economy as a whole showed improvements, although on a regional basis the results were mixed.Key words: Canadian prairie agriculture; Greenhouse gases; Mitigation; Nitrogen fertilizer Use; Trade-off analysisThe Journal of AGRICULTURE AND ENVIRONMENT Vol. 11, 2010Page: 70-82Uploaded date: 15 Septembre, 2010

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.983

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 head0.002
GPT teacher head0.189
Teacher spread0.187 · 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 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
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Agriculture and EnvironmentSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207