Climate Change and the Adaptability of Agriculture: A Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".