Optimal Input Use When Inputs Affect Price and Yield
Bibliographic record
Abstract
Input use has been shown to impact the variance of output and therefore risk. When inputs affect both output level and the price of the output, the variance of revenue and profit depend on each effect and their interactions. We analyze the effect of nitrogen (N) use rate applied to wheat on the variance of yield, revenue, and the price of wheat, when protein premiums (discounts) are applied. We find that N use rate increases the variance of yield, but reduces the variance of price. The net effect of N use rate on revenue and profit is variance increasing, but the variance effect is less than for yield alone. Optimal rates of N are about 60% higher with protein payments compared with a constant wheat price over all protein levels. Risk‐averse producers apply less N than risk‐neutral producers but, because revenue and profit risk is lower with protein payments, the reduction in N is less than if based on a constant price over all protein levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".