Biomass Yield of Switchgrass Cultivars under High‐ versus Low‐Input Conditions
Bibliographic record
Abstract
Switchgrass (Panicum virgatum L.) is undergoing development as a biomass crop to support conversion of cellulosic biomass to energy. To avoid the competition of biomass with food or feed crops, most commercialization proposals suggest that switchgrass should be grown exclusively on marginal lands that are not fit for food or feed production. The objective of this study was to investigate the potential for cultivar × environment interactions that would affect the methods and approaches for breeding and evaluating switchgrass cultivars, including both upland and lowland types, for high‐input versus low‐input types of environments. Biomass yield was measured on 14 cultivars that were present in 28 replicated field experiments representing seven regions, ranging from 75 to 100° W and spanning USDA Hardiness Zones 4 through 7. Region was the most important environmental factor interacting with cultivars, supporting the idea that the north‐central and northeastern United States should have independent switchgrass breeding programs. Cultivars interacted with soil phosphorus concentration in New Jersey and with depth of the A and B horizons in New York and showed mild interactions with rate of nitrogen fertilizer at several locations. Cultivar rank correlation coefficients between the two rates of nitrogen fertilization (100 vs. 0 kg N ha−1) ranged from 0.23 to 0.88, suggesting a possible benefit to breeding and selection without applied nitrogen fertilizer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".