The conditioning environment mediates soil biological legacies, while plant traits mediate corresponding responses among <scp> <i>Medicago sativa</i> </scp> cultivars
Bibliographic record
Abstract
Social Impact Statement Alfalfa is the most widely cultivated perennial forage crop in the world, supporting livestock production and contributing to global food systems. However, soil degradation and declining productivity threaten the long‐term sustainability of alfalfa pastures. Our study shows that plant–soil feedback (PSF) differs among alfalfa cultivars, but that the biotic and abiotic environmental contexts affecting PSF were consistent among cultivars. Identifying cultivars that promote positive PSF can enhance plant growth, improve pasture longevity, and reduce agricultural inputs. These findings offer pathways for selecting resilient alfalfa cultivars, contributing to more sustainable forage production and supporting global efforts to enhance agricultural and environmental sustainability. Summary Plant–soil feedback (PSF) influences plant performance and ecosystem dynamics through interactions among plant traits, soil microbial communities, and environmental factors. This study explores the intraspecific variability of PSF in alfalfa ( Medicago sativa ), focusing on how specific plant functional traits and environmental conditions shape these outcomes. We used a greenhouse experiment with soils from 12 alfalfa stands (ages 4–60 years) to evaluate PSF, defined here as the effect of field soil inoculation relative to sterilized soils, on shoot and root biomass for 20 alfalfa seed sources. We then tested how the field environment mediated PSF and how functional traits affected responses to these biological legacies among seed sources, using independently measured traits. PSF varied among alfalfa cultivars, with positive effects linked to delayed vertical growth and higher root and leaf mass fractions. PSF effects on both shoots and roots were more positive when the inoculum came from highly productive fields (higher NDVI) and more negative when it came from fields with higher soil pH. Older stands also had negative effects on shoot PSF, likely due to the accumulation of detrimental soil microorganisms over time, although the effects were weaker. PSF was dependent on both the environment and alfalfa cultivar. Selection of cultivars that benefit from alfalfa conditioned soils may be important for sustainable management, especially for extending the longevity of forage stands. Directed breeding for traits associated with positive PSF, combined with cultivation practices to support beneficial soil microbiota, may be essential for ensuring long‐term stand health.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".