Nutrient uptake of hybrid poplar in competition with weeds using the soil supply and nutrient demand (SSAND) model
Bibliographic record
Abstract
Mechanistic nutrient uptake models can help gain a quantitative understanding of nutrient uptake by plants under weed-competing conditions. The objectives of this study were to check the applicability of the soil supply and nutrient demand (SSAND) model to predict N, P and K uptake by hybrid poplar (Populus deltoides × Populus × petrowskyana var. Walker) grown with and without competition with dandelion (Taraxacum officinale) and quackgrass (Elymus repens) in a controlled environment, and to determine if incorporating N mineralization into the model would improve N uptake predictions. Simulation results showed that N uptake was underestimated for hybrid poplar by 58 to 73%, depending upon soil type and weed treatment. Incorporation of N mineralization as a model input improved the hybrid poplar N uptake predictions by 24 and 67% in the pasture and alfalfa soil, respectively, when grown without weeds. The SSAND model underestimated P uptake by 84 to 89% and overestimated K uptake by 28 to 59% for hybrid poplar depending upon the soil type and weed treatment. Results from this study illustrate that modeling competition between two species in a controlled environment study is a complex process and that further work is required to understand the underlying processes controlling nutrient supply and uptake in these systems. Key words: Nutrient uptake modeling, SSAND model, hybrid poplar, weeds, sensitivity analysis
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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 | 0.001 |
| 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 | 0.001 | 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".