Evaluation of nitrogen fertilization of hybrid hazelnuts (<i>Corylus ameri</i><i>cana</i> × <i>Corylu</i><i>s avellana</i>) based on leaf nitrogen sufficiency thresholds
Bibliographic record
Abstract
Hybrids between Corylus avellana and Corylus americana are one of several new perennial and winter annual crops being developed as part of the Forever Green Initiative at the University of Minnesota. As a woody perennial shrub, hazelnuts in agroforestry systems can provide a new revenue source for rural landowners; continuous living cover to prevent soil erosion, sequester soil carbon, protect water quality, and provide wildlife habitat; and a delicious and healthful new local food. If hazelnuts are to fulfill their potential, better germplasm and better nitrogen fertilization recommendations are both needed. We modeled these trials after trials by R.E. Worley, who showed that yields of pecan trees fertilized only when leaf nitrogen (N) fell below critical thresholds were maintained with lower levels of applied N, benefitting both the environment and growers’ profits. Results of trials at three Minnesota sites, comparing N applied only when leaf N fell below 1.8%, 2.0%, or 2.2% with annual N applications and no N over 4 years, support using 2.2% leaf N as the critical threshold for N fertilization. Our results showed that whereas only 8% of N applied annually ended up in the harvested nuts and husks, N that was applied to plants that demonstrated hunger for it was more efficiently taken up. Our results suggest a need for more productive germplasm and further research to develop best management practices for N fertilization.
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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 | 0.000 |
| 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 | 0.001 | 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".