Impact of Sinorhizobium meliloti strains and plant population on regrowth and nodule regeneration of alfalfa after a freezing event
Bibliographic record
Abstract
Abstract Purpose The increase in frequency of freeze–thaw episodes with the diminution of snow cover protection due to climate change compromises the winter survival of alfalfa ( Medicago sativa L.). Symbiosis with cold-tolerant rhizobial strains can improve the ability of alfalfa to survive and grow under stressful conditions. Methods Six strains of Sinorhizobium (Ensifer) meliloti were tested in combination with two alfalfa populations bred to differ in their levels of freezing tolerance. Plants of each different combination were grown for eight weeks in a growth chamber before being exposed to temperatures promoting their acclimation to cold. Plants were then exposed to a freezing stress (-11ºC) and regrown for three weeks. Shoot, root and nodule biomass were measured before cold acclimation and three weeks after the freezing stress. Results After freezing stress, the alfalfa population A-TF7 had shoot and root biomasses that were respectively 19% and 15% larger than cultivar A-TF0. Alfalfa plants inoculated with strain NRG34 showed both a larger shoot biomass and a higher nodule dry weight than plants inoculated with any other strains. Assessment of freezing damages on nodules showed that plants inoculated with NRG34 had the largest proportion of undamaged nodules or of nodules with a regeneration zone. Conclusion This study shows for the first time a relationship between nodule and shoot regrowth after a freezing stress, the latter being linked with the proportion of nodules showing less freezing damage. Our results demonstrated that both the choice of alfalfa populations and S. meliloti strains adapted to stress are complementary to increasing alfalfa persistence .
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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".