An Innovative Approach for Modeling Crop Yield Response to Fertilizer Nutrients
Bibliographic record
Abstract
Fertilizer recommendations seldom account for agro-climatic conditions, which are important factors that determine the response to fertilizer and the optimal rate of fertilizer. The nitrogen fertilizer response to open pollinated and hybrid canola types will also impact optimal nitrogen rates. This study used quantile regression to model canola yield response to nitrogen fertilizer. Quantile regression can apply different weights to the residuals, facilitating a response estimation where the agro-climatic conditions are not limiting and the yield response is due to the variable of interest. The economically optimal levels of fertilizers were calculated using the proposed and the conventional least squares procedures of the two canola types in western Canada. Results showed that the effects of nitrogen fertilizer on yield depended on the canola type and on the estimation procedure. Optimal levels of nitrogen for open-pollinated canola were estimated as 91, 115, and 134 kg ha-1 for severe, moderate and low levels of agro-climatic constraints. Hybrid had a higher yield potential, and also required more nitrogen fertilizer (137, 142, and 158 kg ha-1). Unlike conventional approach, proposed approach could benefit producer by recommending less (more) fertilizer when the crop response to fertilizer is expected to be low (high) due to agro-climatic conditions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".