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Climate Change and the Adaptability of Agriculture: A Review

2000· review· en· W2088202725 on OpenAlexaff
Tom Johnston, Q. Chiotti

Bibliographic record

VenueJournal of the Air & Waste Management Association · 2000
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsImpactUniversity of TorontoUniversity of Lethbridge
Fundersnot available
KeywordsAdaptabilityClimate changeAgricultureContext (archaeology)Environmental resource managementAdaptation (eye)Environmental planningConceptual frameworkNatural resource economicsGeographyEnvironmental scienceEconomicsSociologyPsychologyEcologySocial science

Abstract

fetched live from OpenAlex

The assessment of climate change impacts on agriculture has emerged as a recognizable field of research over the past 15 years or so. In a relatively short period, this area of work has undergone a number of important conceptual and methodological developments. Among many questions that have been debated are the adaptability of agriculture to climate change and the importance of land management adjustments in reducing the adverse effects of climate change. In turn, this latter focus has spawned a discussion regarding the nature of adaptation and the ability of agriculture to respond to sudden and rapid climatic changes. In this paper we present an overview of this debate. It is argued that the first generation of climate change impact studies generally ignored the possibility that agriculturalists may adjust their farming practices in order to cope with climate change or to take advantage of new production opportunities. This conceptual oversight has been largely eliminated over the past five years or so. However, questions remain surrounding the likelihood that various adaptive strategies will actually be deployed in particular places. In this paper, we stress the importance of studying adaptation in the context of decision-making at the individual farm level and beyond.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.270
Teacher spread0.216 · 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 designOther design
Domainnot available
GenreReview

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

Citations22
Published2000
Admission routes1
Has abstractyes

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