Effects of Drought Stress on Red Clover-Grass Mixed Stands Compared to Grass Monoculture Stands in Nitrogen-Deficient Systems
Bibliographic record
Abstract
Drought stress is one of the major abiotic stress factors affecting forage production; thus, it is essential to obtain a better understanding of how forage responds to drought. The main objective of this study was to evaluate how legume-grass mixed forage stands respond to drought stress when compared to grass monoculture. A greenhouse pot experiment was conducted using a red clover (Trifolium pratense L.)—timothy grass (Phleum pratense L.) mixed stand and a timothy monoculture stand, where plants were subjected to severe drought (20% field capacity—FC), moderate drought (40% FC), and well-watered (80% FC) conditions for four weeks and subsequently allowed to recover for another four weeks by adjusting moisture back to 80% FC. Both moderate and severe droughts significantly reduced the shoot biomass of the mixed stand, while no difference was exhibited in the timothy monoculture. The shoot biomass and nitrogen fixation capacity of red clover were reduced under drought stress. However, red clover plants subjected to moderate drought were able to recover shoot growth and nitrogen fixation capacity during the recovery phase, allowing more biologically fixed nitrogen and shoot nitrogen production similar to the plants growing under well-watered conditions. Overall, the results demonstrate that the inclusion of legumes in forage mixtures enhances resilience to moderate drought stress.
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 | 0.000 |
| 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".