Comparison of Three Statistical Models Describing Potato Yield Response to Nitrogen Fertilizer
Bibliographic record
Abstract
Estimation of optimum fertilizer rates is of interest because of growing economic and environmental concerns. Optimum fertilizer rates can be determined by fitting statistical models to yield data collected from N fertilizer experiments. We evaluated quadratic, exponential, and square root models describing the yield response of potato ( Solanum tuberosum L.) to six rates of N fertilization (0–250 kg N ha −1 ) with and without supplemental irrigation at four on‐farm sites in each of three years (1995 to 1997) in New Brunswick, Canada. Economic optimum N rates (N op ) varied among sites and models. The proportion of variability ( R 2 ) explained by the three models was similar. The quadratic model, however, calculated a greater N op value (175 kg N ha −1 ) averaged over all sites than those calculated by the square root (123 kg N ha −1 ) and exponential (80 kg N ha −1 ) models. Regression residues of the quadratic model were closer to a normal distribution than those of the other two models, indicating a less systematic bias. Economic losses were greatest when the quadratic model was the most appropriate model, but the data were fitted to the exponential (loss of $204–240 ha −1 ; all values in Canadian dollars) or square root model (loss of $58–201 ha −1 ). We conclude that the quadratic model is the most appropriate for describing the potato yield response to N fertilizer and predicting N op for areas with a ratio of the cost of N fertilizer to the price of potatoes similar to that in Atlantic Canada.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".