Genotypic Effects of Fertilization on Seedling Sweetgum Biomass Allocation, N Uptake, and N Use Efficiency
Bibliographic record
Abstract
Screening and selecting tree genotypes that are responsive to N additions and that have high nutrient use efficiencies can provide better genetic material for short-rotation plantation establishment. A pot experiment was conducted to test the hypotheses that (1) sweetgum ( Liquidambar styraciflua L.) families have different patterns in biomass production and allocation, N uptake, and N use efficiency (NUE), because of their differences in growth strategies, and (2) sweetgum families that are more responsive to N additions will also have greater nutrient use efficiencies. Seedlings from two half-sib families (F10022 and F10023) that were known to have contrasting responses to fertility and other stress treatments were used for an experiment with two levels of N (0 vs. 100 kg N/ha equivalent) and two levels of P (0 vs. 50 kg P/ha equivalent) in a split-plot design. Sweetgum seedlings responded to N and P treatments rapidly, with increases in both size and biomass production, and those responses were greater with F10023 than with F10022. Growth response to N application was particularly strong. N and P application increased the proportional allocation of biomass to leaves. Under increased N supply, P application increased foliar N concentration and content, as well as total N uptake by the seedlings. However, NUE was decreased by N addition and was higher in F10023 than in F10022 when P was not limiting. A better understanding of genotype by fertility interactions is important in selecting genotypes for specific site conditions and for optimizing nutrient use in forestry production.
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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) | 0.001 | 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) | 0.001 | 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".