A modelling investigation into the economic and environmental values of ‘perfect’ climate forecasts for wheat production under contrasting rainfall conditions
Bibliographic record
Abstract
Abstract With increased investment in improving climate forecasting techniques, it is essential to find ways of quantifying the maximum benefit of climate forecasts for a given industry. This paper describes an approach to quantify the value of ‘perfect’ climate forecasts to direct nitrogen management in a wheat‐cropping system at two Australian locations with contrasting annual rainfall. For annual wheat‐cropping systems, and compared with the N management based on optimal N rate derived from long‐term climatic conditions, N management based on ‘perfect’ climate forecasts can lead to an average benefit of $ 65.2/ha/year at Walbundrie (annual rainfall 560.0 mm) and $ 66.5/ha/year at Wanbi (annual rainfall 314.5 mm). Generally, the economic benefit is highest in extreme (wet and dry) years and lowest in normal years. At the high rainfall site Walbundrie, where average N‐application rate is high, the maximum yearly benefit was from significant saving through reduction in N application in driest years. At the low rainfall site Wanbi, where average N rates are low, the highest benefit was from both yield increases in the wettest years and saving of management and fertilizer cost in the driest years. Such optimized nitrogen management has little impact on excess drainage, but it can have significant impact on reduction of excess nitrogen, especially in high rainfall areas. An excess N reduction of 1314 kg N/ha at Wanbi and 1538 kg N/ha at Walbundrie can be achieved in 114 years. The significant reduction in N excess at Walbundrie may have profound environmental implications. Copyright © 2007 Royal Meteorological Society
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".