Risk and Nitrogen Application Levels
Bibliographic record
Abstract
Stochastic weather and soil conditions are the suggested reasons why farmers tend to apply more than the recommended levels of nitrogen. This study found that uncertainty plays a role in the application decision of farmers but not in the manner typically assumed. Using a time series of field trials of corn yield to nitrogen for the same site, nitrogen was found to be a risk‐increasing input suggesting that uncertainty should decrease, rather than increase, a risk‐averse farmer's rate of nitrogen application. Similarly, viewing risk as a profit shortfall, in which fertilizer acts in the role of insurance, was also not supported with the empirical results. Instead, the key role of uncertainty is its impact on expected profits. Increasing application rates leads to lower returns in most years but the increase in profits generated under favorable growing conditions results in greater expected profits with a high application strategy. Les conditions météorologiques et pédologiques aléatoires seraient les raisons pour lesquelles les agriculteurs tendent à appliquer des doses d'azote supérieures aux doses recommandées. Selon la présente étude, l'incertitude joue un rôle dans les décisions d'application des agriculteurs, mais d'une façon différente de celle généralement supposée. À l'aide d'une série chronologique d'essais en champ mesurant le rendement du maïs en fonction de l'azote dans le même site, nous avons trouvé que l'azote était un intrant qui augmentait les risques, ce qui laisse supposer que l'incertitude devrait faire diminuer, plutôt que de faire augmenter, la dose d'application d'azote dans le cas d'un producteur qui craint les risques. De même, considérer le risque de baisse des profits où l'engrais assume le rôle d'assurance n'a pas été appuyé par les résultats empiriques. Le rôle clé de l'incertitude est son impact sur les profits prévus. L'augmentation des doses d'application entraîne une diminution des rendements la plupart des années, mais l'augmentation des profits générés dans des conditions de croissance favorables entraîne des profits prévus plus élevés grâce à une meilleure stratégie d'application.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".