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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".