Physiological Comparisons of Switchgrass Cultivars Differing in Transpiration Efficiency
Bibliographic record
Abstract
Production of forage species like switchgrass (Panicum virgatum L.) is often relegated to areas with minimal inputs of water and fertilizer, therefore, selection should be based on efficient use of these resources. This study examined genotypic variation in switchgrass transpiration efficiency (TE), defined as the weight of dry matter per unit of water transpired, under conditions of water and N stress. Since reports show TE to be correlated with specific leaf weight (SLW) and leaf ash, these easily measured traits were assessed for their potential as predictors of switchgrass TE. In one greenhouse experiment with nine cultivars and two outdoor experiments with two cultivars, plants were grown in closed containers in a soil–peat mix or solution culture and subjected to water or N deficit. Cultivars differed in TE; however, TE did not differ between water stressed and well‐watered conditions. With decreasing N in solution, TE also decreased. Cultivars differed in their values of TE when grown in nutrient solutions containing 10.0 and 1.0 mM N, but not at 0.3 mM N. Transpiration efficiency was positively correlated with SLW in each experiment and across all experiments Correlation between TE and leaf ash was inconsistent, with a negative relationship in the water stress experiment and a positive relationship in the N experiment. The results show differences in TE among switchgrass cultivars and show that SLW is consistently predictive of TE.
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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 | 0.000 | 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".