Climate change affects cell‐wall structure and hydrolytic performance of a perennial grass as an energy crop
Bibliographic record
Abstract
Abstract Perennial grasses, such as Panicum maximum , are important alternatives to dedicated energy crops for bioethanol production. This study investigates whether future climate conditions could influence P. maximum cell‐wall structure and hydrolytic performance. To analyze interactions with environmental factors in field conditions, a combined Free‐air Temperature and CO 2 Controlled Enhancement (Trop‐T‐FACE) facility was used to investigate the isolated and combined effect of elevated atmospheric CO 2 concentration (eC) (600 μmol.mol −1 ) and elevated temperature (eT) by 2 °C more than the ambient temperature, on cell‐wall composition, cellulose crystallinity, accessibility, and hydrolysis yields. The elevated temperature treatments (eT and eT + eC) exhibited the most pronounced effects on the P. maximum cell wall. Warming reduced the starch content and the crystallinity index (CI) of cellulose and increased the cellulose content. Fluorescent protein‐tagged carbohydrate‐binding modules analysis demonstrated that warming improved total cellulose surface exposure/accessibility in eT and eT + eC by 181% and 132%, respectively. Consequently, glucan conversion yields were improved by 7.07% and 5.37%, showing that warming led to lower recalcitrance in P. maximum biomass, which positively affects its use in biorefineries. This work therefore provides important information from an ecological and economic point of view, allowing us to understand the mitigation process applied by this forage grass under future climate conditions. It might assist in selecting tropical forage grasses that are efficiently adapted to climate change, with a positive effect on bioenergy production. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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 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 | 0.000 | 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 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".