Unrealized Potential: A Review of Perceptions and Use of Weather and Climate Information in Agricultural Decision Making
Bibliographic record
Abstract
Abstract This article reviews research on agricultural decision makers’ use and perceptions of weather and climate information and decision support tools (DSTs) conducted in the United States, Australia, and Canada over the past 30 years. Forty–seven relevant articles, with locations as diverse as Australian rangelands and the southeastern United States, ranging in focus from corn to cattle, were identified. NVivo 9 software was used to code research methods, type of climate information explored, barriers to broader use of weather information, common themes, and conclusions from each article. Themes in this literature include the role of trusted agricultural advisors in the use of weather information, farmers’ management of weather risks, and potential agricultural adaptations that could increase resilience to weather and climate variability. While use of weather and climate information and DSTs for agriculture has increased in developed countries, these resources are still underutilized. Reasons for low use and reduced usefulness highlighted in this literature are perceptions of low forecast accuracy; forecasts presented out of context, reducing farmers’ ability to apply them; short forecast lead times; inflexible management and operations that limit the adaptability of a farm; and greater concern with nonweather risks (such as regulation or market fluctuation). The authors’ main recommendation from reviewing this literature is that interdisciplinary and participatory processes involving farmers and advisors have the potential to improve use of weather and climate DSTs. The authors highlight important gaps revealed by this review, and suggest ways to improve future research on these topics.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".