Predicting nitrogen fertilizer requirements for corn by chlorophyll meter under different N availability conditions
Bibliographic record
Abstract
Nitrogen management strategies that enhance fertilizer use efficiency and maximize profitability in corn require a rapid and accurate method to determine the crop N needs of current hybrids. The objective of this study was to evaluate the potential of a portable chlorophyll meter for predicting N fertilizer requirements for corn grown under varying levels of N availability. Several crop management treatments were imposed in an attempt to create conditions ranging from low N availability (oily food waste application in spring and fall, application at different rates in spring) to high N availability (continuous fertilized corn, winter wheat cover crop). Different corn hybrids were sown at different sites (21 site yr) and varying N fertilizer application rates were applied. Chlorophyll meter readings (CMR) were taken at the 5th to 6th leaf stage (V6) using a SPAD-502 chlorophyll meter. The crop management treatments, corn hybrids and their interaction significantly affected the chlorophyll meter readings. A high inverse correlation between chlorophyll meter readings and maximum economic rate of nitrogen (MERN) was observed (r = −0.87). The main new and unique aspect of our research is the development of a linear model for using chlorophyll meter measurements to make N fertilizer recommendations (MERN = 348.47 − 8.5304 × CMR ) for corn production under varying degrees of N availability in Southern Ontario. Key words: Chlorophyll meter, soil organic carbon, nitrogen 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".