Resilience, Stability, and Productivity of Alfalfa Cultivars in Rainfed Regions of North America
Bibliographic record
Abstract
Resilient, stable, and productive forage systems are needed to endure increasingly frequent climatic extremes. Resilience is the ability of a forage system to withstand a climatic crisis with high yields, stability is the minimal variability of yields across normal years, and productivity is the average yield across normal years. The goal of this research was to quantify resilience, stability, and productivity of alfalfa ( Medicago sativa L.) cultivars to identify superior ones. Forage yield means from alfalfa cultivar trials from 11 US states and one Canadian province over 19 yr (1995–2013) were analyzed using linear mixed models. Locations with an extreme crisis year were identified, and quantitative measures for resilience and stability for each cultivar were calculated. Productivity, stability, and resilience were different among cultivars across locations, showing that some cultivars were consistently superior for each variable. Cultivar stability was not associated with productivity, and it was negatively associated with disease resistance. Cultivar resilience was negatively associated with productivity, and not associated with other traits. Cultivar productivity has increased with year of release of cultivar, stability has not changed, and resilience has decreased. Therefore, stability and resilience are different dimensions, explained by different traits. A coordinated evaluation effort across locations is needed to test and improve cultivar resilience in the future, and develop alfalfa cultivars more profitable for the long term.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".