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Record W3209517843 · doi:10.1139/facets-2020-0097

Opportunities and trade-offs for expanding agriculture in Canada’s North: an ecosystem service perspective

2021· article· en· W3209517843 on OpenAlexaffvenueabout
Krishna Bahadur KC, Arthur G. Green, Dan Wassmansdorf, Vivek Gandhi, Khurram Nadeem, Evan Fraser

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEcosystem servicesAgricultureSustainabilityBusinessClimate changeNatural resource economicsCarbon sequestrationEnvironmental resource managementEcosystemGeographyEnvironmental scienceEcologyEconomics

Abstract

fetched live from OpenAlex

Climate change will create warmer temperatures, greater precipitation, and longer growing seasons in northern latitudes making agriculture increasingly possible in boreal regions. To assess the potential of any such expansion, this paper provides a first-order approximation of how much land could become suitable for four staple crops (corn, potato, soy, and wheat) in Canada by 2080. In addition, we estimate how the environmental trade-offs of northern agricultural expansion will impact critical ecosystem services. Primarily, we evaluate how the regulatory ecosystem services of carbon storage and sequestration and the habitat services supporting biodiversity would be traded for the provisioning services of food production. Here we show that under climate change projected by Canadian Earth System Model (CanESM2) Representative Concentration Pathway 4.5, ∼1.85 million km 2 of land may become suitable for farming in Canada’s North, which, if utilized, would lead to the release of ∼15 gigatonnes of carbon if all forests and wetlands are cleared and plowed. These land-use changes would also have profound implications for Indigenous sovereignty and the governance of protected and conserved areas in Canada. These results highlight that research is urgently needed so that stakeholders can become aware of the scope of potential economic opportunities, cultural issues, and environmental trade-offs required for agricultural sustainability in Canada.

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 score0.158
Threshold uncertainty score0.509

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 head0.072
GPT teacher head0.251
Teacher spread0.178 · 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

Citations20
Published2021
Admission routes3
Has abstractyes

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